{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Express sklearn pipeline as codeflare pipeline\n",
    "Reference: https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Comparing Nearest Neighbors with and without Neighborhood Components Analysis\n",
    "\n",
    "An example comparing nearest neighbors classification with and without\n",
    "Neighborhood Components Analysis.\n",
    "\n",
    "It will plot the class decision boundaries given by a Nearest Neighbors\n",
    "classifier when using the Euclidean distance on the original features, versus\n",
    "using the Euclidean distance after the transformation learned by Neighborhood\n",
    "Components Analysis. The latter aims to find a linear transformation that\n",
    "maximises the (stochastic) nearest neighbor classification accuracy on the\n",
    "training set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": 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jZlG48FB8fZvStGlxhgyZkeq5Uqb/+mrWbMXQodMJDByIr28LWrSowIAB77N161JnfXog0BmzeSFr17qOU/Ecagq5p1JlkMwL3Qh/Nc7xdyM6nR4hzCmOmBHC9WqJOp0eu30d4ANY0IbMaQm4SZNHadLk0Tues27dHJYseRWrNY7atbsyZsyX+Pr6u7xGy5Z9aNmyT5px3sr1Seh0alXH3EBNIfdkHderrb0y6+a7kZzTvPnjGI1rEeJ1YAkmUxjduo10eb7FkghcA/YAcUDbFJNB7nT48EYWL34Xs3k7DscNjh3zZ9as4ZmOs23b/2EyfQm8ByzCZHqKnj1dx6l4DpWoPZ0qg2ReoRjSWqnw0qUTHD++jfj4qAw1s3fvz6xbN5tr186lefz69QsABAaWYurU7bRqdZUGDX7mmWfGEhb2kst2Dx/eBDwL1ELrVb+H2ew6psOHN2GxPANUB3yx2SZz9OjvGXoNKZUsWZl3391Cy5anadhwHc8/P5X27Z/NdDtKzlOlj9wgbBVsaOecGKPKIOm6baVCKeGLL0azdesyDIZywFkmTvyJSpVC0ny6w+HghRcaERFxFghi/vxxjBo1n6ZNH2PkyIZcuXIOKMX8+eMYPfpLmjXrRfHi5Rk2zPXkkZS06dO70f6QCOAQ2n36tAUEFMdo3IzFknz+QQoWLJ7Rr0YqwcE1GDnyi3t6ruI+qkedW6h9GDPPuVLhgQNr+eOP37BYTpCQsIuEhBlMn/4/l09btmwiERFxwDngCDCbmTOfZ+nSV7hyJcF5/CjwKZ98MjTTYdWu3Q44AISi9az7UqhQoMvz27YdSLFi5zGZOmA0DsJkGsiQIR9m+rpK7qV61LlJ6EY1gzGz/GO4fPkEDkcbIPnmWw+uX++PlDLN3UPOnDkAdAaS111+BLt9AGfPHgI6AF8DkUBj7PYYAJKS4tm6dTHx8TeoUyeUypUfcBlSZORFYBBQF4gG/kdSkjb7MDExlq1bF5OYGEO9eh2pUKEB3t5+TJ26lT//XEliYiy1a48nKKjq/X5llFxE9ahzm7BVqmedGe03UKZGOXS6tWg38AC+pkSJ2i63eKpatRnwA1oyBvgKgyGA8uUbAkuAn52PPY4Q3iQlxfPSSw/y1Ve/smxZJJMmPcyuXd+7DKls2TqYTL8CXYHnEGIXQUG1SUiIYdy4ZixevJFvv73GxImd2LPnJwCMRh8efPAJOnQYqpJ0PqQSdW6kyiCZUme8jU6PPIqXVxW8vWtQsOCbjBu3xOX5YWETqFChHBAMlEGIVxg7dhHFi5dDiEbAauAD4Ge8vQvwxx9LuH69DBbLDzgcH2CxfM/8+eNdtt+kyaM8+GAbvLwq4u1djcDAuYwePZ/Nmxdy40YtLJbvcTimYbF8w/z5L2fxV0PJjVTpI7cK3ZhiBiOoUsjdPfHtA3SdN5KY6BuULFnFueQnXLr0D0uWTCY6+hohIe3p0WMMOp2OqVP/4MyZA1y9epZatdrg6+vP6tUfoNPVxW5P/lpXwWZLID7+BjZbFW59D6qQmOh6TpgQgsGDZ9Cr1zji46MoVaoKBoOR2NjkdkjRThSglWOWLn2P+PgYWrToTqdOQ9Wmr/mI6lHnZu03wGMr1PC9DAoYtJOytctiNGprMEdGXuLVV1uzd289/v13JCtXruLLL28Nqytfvj4PPNDj5sSSOnVC0eu/RtutOwKDYTR163aibt32GAyLgT+ACLy8xlCvXud04ylcuDRlytTCYNBGfNSv3wEvr/nATiAcL68XadCgE5cv/8vEie3Zv78l//zzHN98M4fvv5+apV8bxbOpRJ0XhK1KMTFGJey76rjeuVmDZPfuH7BaOyDleKArZvNyNm6c6/KpFSo04IUXviAgYBAmU03q17cxYsRcKlZsxMiRnxMQ8IzzuGT48M8zHVq1as157rkZFCrUH5OpNiEhBRg6dCbbtn2L1foU2pI73TGbF7N2res4lbxHlT7yivYbtP9vjrdOpt4e3yFsJXz/KEIIhEg5i9Gebjnh8OHfiYq6DJg5fnwnsbHX8fX1p3HjnpleNzotLVo8TosWqdd/1mK6PU7Vx8pP1Hc7rwndqJVDHltxs+eopKFQNI0b98Ro3IwQk4DlmEw96dhxmMunbN68iLVrvwQ2AdeJi3uIV15pl+2hPvhgP0ympQgxFViGydSPhx92FadEvbPKe1SizsvCVoI+uX6tfnlTCd1IQAUjU6b8QYsWF6ld+xv69RtA//5vu3zKjh3LgP8BD6CNyZ5OXNzFm4+Hh5/k5MndJCXFu2jhFiklly//y8mTuzGbE+56bokSFXnnnc00bXqcOnW+Y8CAl3j44VHJLaX+9+hKdd8iD1Klj7zu0VW3Pr45WQZUSQQI3UhxGcrIkXPJyNcjIKAEcIhbU7+PIoQJKSWffz6c7du/R68vhcEQyeTJvxIcXDPNdqSUfPrpEP788yf0+hIYjdFMnrzuruOjg4NrMHr0wuQWbv0fugECo+58QqplB8jQ61M8l+pR5ydhq7Te1s0x2KrHRfsN2rsOffJa1q6/Jk899RFeXseAB4EhQBd69BjOX3+tYseO7Vgs/5KYuJ/Y2Ff58EPXm+Hu3Pkdf/21D4vlJImJB4iJeZEZMwalE6gzNp1NS87J5a20knSy5DKYWoEx11M96vwoedGiXHDjMS4umunTexIVdZkHHniEfv20XVNsNgvbt39LdPQVatR4kCpVmmjHLTa2f7ud6CvR1GxVk8qNK6d/kTTedVgsSWzf/i1xcZHUqtWGihUbUqBAAHPm/MPXX48nJuYKLVrMp3nz3qxc+S5WawegoLORx4iIcD3h5cKFvzGbOwN+AEjZi/DwyWmceSu5JjX9ie3r1pAYk0jdi3UpG1g2/deVrP2GXPG9VlxTiTo/S07Y4JFvk+Piohk4sDxSVgNCuXjxc44d28GkSRuYOLEjFy4IbLZ66PU9GTRoKi0fCmRC9wlcNFzEVseGvoeewdMG0+qJVhm/aNgqLEkWXq35MRGXArHZqqPXd2HYsE9p1qwXBQoEMGRI6qFxJUpUQsoJwAS02vVSChUq4/ISwcE1MZmmYDaPBwoixDJKlUoukziTs84ObTdBYBSJsYmMbzCJqMs1sFnL8+2Edxj3w/PU65CJndFdfq/BU77fimsqUSuam0uDek4d+7PPnkLKcsAOtCrdUP75pxF//rmSixdtmM1bAB12+wAWLGiDV6NnucQlzL+ZQQf2p+3M7zg/c4ka2PHtDiKuFMRsXgsI7Pa+zJv3OM2a9Urz/ISEKIQQSFkJKAncICnJdVWxWbPHOHBgM9u3V8JgKIHJlMCoUb/iqua8cd5GIi82xJq0AgC7tRtfDB3Bp6czkahTSpm0U32/wd3fcyVtGUrUQogzQCzaYE6blDLthXyV3C/MWQbI4V7X9esX+PrryVy/Hk69eg/yyCMvEh19BajJrVspVQErUVHhOBzVUhyvhtkcTcz1aOyl7fCED1z0gk7xJEUmuVwlz5XY67FYLUFoezhHAi1JSLjGrVJE6rbi428APYBhwA2gCBZLfZftCyHo3n0EN5JOExN/ldb9H6TkiAMgDqYdz7V4rEm1UhypQXxUbIZfz12FubrZDCppe47M9KjbSCmvpX+akie47HVl/S9vXFwkL73Ukri4/jgcXTl9egZXrpyjTZunOXnyRbRxyw2BiQhRiHr1OrB06TvAZqA+ev0bVK7clnK1KmB9yQDiRXA0hJ2TCAiKQCBc30tL4+WUrVMWh/Uj4BW0XVjeIDC4mHZjLo0NHGrXbovB0B2LpRdQFYNhDLVrd3D5eq9cOcNrbzch6aU4ZCXJpTePkmC/Tq9X0u6x1+tQh19mfI4loQdQDi/TOOp1rJvelzXzXCZtlbDdTZU+lPSl2cvOul/e/ft/xWKpj8OhjWE2mx9i8+aSPPtsHMePb+OPP7oDZnS6gkye/BPBwTUYM2Yhs2cPID7+CtWqtWH06K/Z89OPGPRdsdne0hq2NSfmYkXk8l5p96g7rHNu25Xa5X8uo/d6BLt1gvNII2Ku1tE+TLUYltZm5cqNef75T5g37zGSkm5Qp04nRo6c5/L17tixDEvvJOR47a+HuaaZNR3XuEzUNVvXZNBnYSwa0wlLYiINOocwdJ7rUSVZwk3vrJS0ZTRRS2C9EEICc6SUdyw0IIQYDAwGKFq2aNZFqHiOLBgtcujQBmbNGkZsbDiVK7dk9OgFSCmRMu02RoxYzIgRdx5v2LArX3zRNdUxKSU6XcofaUFyft6z5yfmzh1NfPxVatRox6hR825uC5AW6Uj5mSDF1t3aKIp1HSCm0M1DzZv3pnnz3ndp8TYi9cdS3n34nDCArmASQloQBRIzfp37ddebkKASd87I6DjqFlLKhmjbXgwTQtxxd0ZKOVdKGSKlDPEv5nobeyUPSDVNPeXMx7sLDz/FBx/0JTLyE6zW//jnnxpMmfI4DRp0xmjch073BrAao7EnDz74DHp95t7whYR0x2j8AyHeAlZjMj1K27aDOX/+CDNmDCQqaj5W62mOHSvO9OlPu2xHp9fhsK9C2637R+BhJPrUJ+nvfeZf8+a9MS7zhmkCfgBTHxNdhnZxef6xrcf44qUviP0+FssJC/uS9jHnhTn3dO37kvL7nsnvvXJ/MpSopZSXnP9fAVYBjbMzKCUXSZ5Eo0t/qvrx49sQogPQESiM3T6VM2d2YjL5MXXqNpo2PU/Vqp/zyCMdGTp0ZrqXjoy8yJkzB25O2S5UqDhTpvxB/fqHKFfuA3r06MqAAdM4enQzUoYBrYEi2Gwf8vfxtUj/aACunb/GmQNnsCRaANi/Zj+IHsDfwFygD9bEuNQXD93oXEsl84oXr8A7E3YR8ldTqs+rzhNDn6DXq7fKHlfOXOHsobNYzVYADv52EMsgC4QAxcH6vpUD6w7c07WzVJoTqFTSzg7pdlmEEH6ATkoZ6/y4A/Bmtkem5C5p3ohK/ba4QIFA4F+0wUN64DQ6nRGDwUjRomUYNWpBhi+39PvX+HntdAwlTegjDbw+biPly9fnt98WcuTI7xgMpVizZhYPPNAVP79AdLp/uDX1+zjefoUQQrBozLesn/07BmNxDMZoJm0ej38xf9BdAPtW5/l/gs50ZxD+Malq1ZkRHFyT8UtfBP2tZC+lZPaw2WxfsR19UT0+Vh/eXPcmBQML4vWXF1a0xM1x8Cvsl+lrZhs1RjvbZaRHXQLYJoQ4CPwF/CKlXJu9YSm52s2eViQpe1kNGnShXLlATKZQ9PqxGI1tefrp6eh0mVvJ4NixLazZ+THWf80kHo8h7qNI3v+sB0eO/M7atd9gtZ4gMfEIcXHv8MEHT9Cs2WOUKmXGZOqEXv8iRmNXBrw/iAPrDrBh7gGsSadIjDlB7LW3mdZzNk9++CRexsOgaw26EUAHuoxsfWcg7Tfcc686rZ7nzu92svPPnVhPWUk6lkTUkCg+fvZj2g5qS+DRQIw9jehH6TE+aWTQ1PSmnLtJyvKI6mlnmXR71FLK08A9jqxX8rXbJtHo9XreeOMntm//lhs3LlO9+lKqV2+Z6WbPnz+KbO+AYs4Dj8O1p85z7twRHI72QPLN7D5cvToAg8HIhAmr+eijx4iJWU/z5sNoNagGP3+8Gru1M1DYeX5fIk4/T4GAAnx66n0+evwjYq+touUTnQl7LSztYO6xV21p8Qu7vtlMfFQ8ddrVIbhmMOeOnsPczXxzJrrsK7k47SK+/r58sOMDtn2zjcTYROptrEe5uuUASIpPYteKXSTFJlG3fV2CqgVlKo5slYNDPPM6NTxPyX4pyiKG73vSuvWTzs/u7Re2dOnqiK902tySQOAHKFw6iODgGuh0nwFRQACwisKFq2A2JzLslbKYy8dDM1i27Ahx47tSp20d9F4rsVmigULA9xQrVwar2cqUx6dwyXAJx0MOVn28irK1y/JAjwfuDCaNESDpMZsTeOXh4VwtEo6jogPxpmDskrGUrlYa0ycmzC+ZwRfESkHJaiUB8CnoQ/sh7VO1kxSXxPiW47kRdANHGQe6yTpe+u4larepfQ9f1WyWsjS2sifYVdLODLV6npKz0rz5mDm1a7elXYNn8arsjW/9Qvg+F8DY51ZRp04obdr0wMurKr6+D+DrO5px45bw9dcvaUl6F/AFsAF+mfMLDbo0oNWT1fDyqYSPfz38Asfy4vdD2bFsB5eMlzBvNGOdZcWy0nL3URaZHAGyZcsirgRdxrzOjPVzK5YlFuaOmUuLvi1oVKsRxipGfBr64P+hPy988YLLdjbM3cD1qtcx/2LGOseKeZ6ZL8Z+keE43ObRjN+AVjSqR624h8sJFZCRXtbTfT+iU5vhxMRcITi4Jr6+Wo92wID36dJlCDExV53H/Vm58h2oBDwPXEVbpTRRu3n37Own6TYmlNjrsZSpVQafgj4c3ngYW23brTDqQMK1uyzuH7qR8M/q8t2PbxGTcIUmdcIIbTMYIQTh4Sf57qdJxCRcoVmd3rRtM5DomCtY61hTtR9/LR6dTscL81/g0olLJEQnUKZ2Gbz9vF1eNupq1B3txF7LoqnlOcFlLxtUTzs1lagV90pZx4RMJe6SJStRsmSldI5L6vcrw1+vAqOANsC7oA/Q37yJWapKKUpVKXXz+TVb10TfTY+9vx1qgv4VPdXbVXf5EiIvRvLK5CYkDItFVnNw4q0dREWH07bVQF6eHELiyFhkVQcnJu8gKjac2rVb8+McLyyPW6ASGF4zUKudtpaHEILS1Uu7/nqlULddXdYOXIvlUQuUBa8JXtRtlw1Ty3NCyqVm1ciRO6hErXiWtBJ3jP9tvS3I2C+vc2uqBUnoOulwvOMcodESRHXhcrGmSiGVGDpjKPO6zyMpMonq7aszZtEYl1fZuXwn5u7xyDe09s0N4/ml+Ud4GwpgeSQR+brzeL14fn7oQ8I+e4ABZQawqMMizNFmanWtxfAFwzPwelKrG1qXp157iiUPLcEaZ6Vuj7oM/WRoptvxOGp1vzuoRJ1HnNpzik/6LyTyQgTl6lVi9LJnKRJcJNuve3zbcT4d9inRF6Op3Lwyo+aNolBx1zfWjm05xmfDPyM2PJaqLasy8ouR+Bf158imI8weOVs7/mBVXvjiBQoWKXhn4gbnzTt/0vqlXbXqPZb9OhGH2Y5/kD/vNX4P6ZAY/AxY0Ca04MXNkui+NfuY++JcEq4lUDu0NsM/H45vIV9a9mlJyz4t70jme37aw7xx80iITKBux7oMmzUM6ZBam8m8QEqH9s9Lpj7u0JJ226fb0vbptple2e92HQZ3oMPgDvfdjsdSq/sB6mZinhB9JZo3233A5ROTMMef4OSfXXiz3XQcjnsd45sx185d452e73Bl8hXMR8wcr3Kcd3u96/L8K/9d4b1e73H13askHU7iaJmjTO0zlfBT4UztPZWrU5zHg47yfr/3XV+44/oUY3Rv2bdvDUtXv4rjWzucgpgHYxjfejyNHm6E4TcD4gMBa8HUy8RDgx7i/NHzfPi/D4mcGUnSoSQOeB9gxsAZqdpMmfzOHDjDjIEziJwdSdKBJPbL/cwcMpPGjzbGa6UXzADWgOkxX0IfGkKTJmF4LTPBDKEd7+1L+6c7ploIKquSa55M0rdLvhGdD6evqx51HnDyr5NoQ937AuCwv8m1c7OICo+icFDhuz73fhzfdhzxkIBHtM/tH9g5V+AcSXFJeBfwJuZaDDFXYiheoThGHyPHth5DtBfwsPP8j+yc8j3FoQ2HoBPgXGPJPsPOP77/YLPYMBhd/IiGbryjlrllyyIIQ5uhDjAL4gvEE1AygHe3vMviSYuJWh9Foy6NeHTco/w681ccvR0Qqp1u+9jG4ZKHb7YXFR5F3I04SlQsgZfJi0O/HcLez67VuQHrDCsHKx+k+DfFefv3t1ny5hJifomhaZ1BPNxpPDqdjrdf28HCpS8QE3eNlo360ePlBoCWqCMvRZIQnUDJSiVdv04lbflsdT/105EH+AX44XCcB6xo78Gv4LAn4lPQJ1uv6xvgC/9xa0b4Be24l7cXq2esZtmkZehL6NHH6Zm4eiJ+AX7a+Q6093JnQeelw7+IP+K0uHX8DOhNevRet9elb5NcFvktFEL24L85GvZxa6b4KcAEOp2OoKpBvPTNS6me7hfgh/6UHpt0jvA4CaYAbar44gmLWfvZWvTF9HjbvZm0ZhK+Ab7ot+qxS/vN9r0DtFEZZWqV4ZVlr9yKJ0qHlJINf8zlxPEdGAp7sW7TpzSPfJXihYsy/8X5bPpyE/rCevwMfkz+dTLFKxS/929GfpVPpq+r0kceULV5Vao3L4bJrxVCvILJrzk9xnfP9kRdr0M9yhYui6mDCfGqwNjaSN93+3L24Fm+++A7rEesJJ1IIn5GPO899h71O9cn2CcYUyftfFNbE/2n9iekewhBXkEYOxu1dtoZefL9JzP+dr69tn3V4+88jtdJL62H/BLQGlr3TWPqt1Pzx5tTLLIYxh5GxCsCYzcjz7z/DAfWHWD98vVYT1pJ+ieJ6DHRTPvfNFr2a0mRi0Uw9jQiXhYYHzHyzJRn7mxYaG/Jd+/+kU3HF2I7YybpvzhuvHCZDwdN569Vf7Flwxasp60knUwickAkMwbNuIfvgJJKyunr+rw1Rluktw7uvagUUklO2TMly9tVXHPYHfzx9R9cPXuVio0q0rBLwxy5rs1qY+virURejKRqs6rUDa3L5kWbWfDbApKWJGknSdD56vjy6pcYjAa2fLWFG5dvUL1FdWq31WbRWc1Wtny1hajwKKq3rH7Ps+viIuNYMGIBURFRNH6kMZ2Gd7rr+fHR8SwZu4SoiCia9W5Gq/6tWP3BapZeXor9Q7t2UizoS+hZmrCUuKg4loxdQvTVaFr2bUmLPi3Sbvj7nqz47l2WV5yEfM/5O3YFjNWNdB/ZnRX2FeDc34Bw8K7jzVdXv7qn16ykI5eM0e7dW+x1tc2hKn3kETq9jtZPue49ZheDl4G2A9qmOlaqSinkJAnXgSLARq1EYPIzIYSg3aB2d7TjZfIi9NnQ+46nQOECjPx6ZIbOtVltvBv2LucTz+Oo4eDwmMP4+vtSqmopDEsM2OPsUAD4CYpWKYrNYuPtR97mkv0SjqoODo84jG8hXxp0bnBn42GrKLUnGONaX8wT48EXWC0oXqU4QVWDtKnir5rBR2u/eBVV9sg2Lsdoe2bCTotK1EqWq9a8Gh36d2BdzXUYqhhw/ONg3HfjPG5kws7vdnLOeg7zH9qu5WyD2U/MZt6ZeTT5tQm7qu/CUNYAZ2DML2PY9s02LhouYv7def7vMHvIbOb+e8eGRwA0e8ePv3Z3Zl+VNehKeaG/7GDU2lEE1wrmz7V/cqDaAXSldegv6Hlhreup4koWSq5p32WIpydSiVrJFk++9STtnmxH1OUogmsF419U2/Un5moM3737HdfCr1G3ZV06PdcJnU7H+aPn+ejpj4iJiaFGgxq8sOQFDAbXP543Lt9g+ZTlRF6JpMFDDegwuEOm/xBEX4nGXtd+605NfYiPiEcIwfDPh9PjaA9ir8dSrm45/AL8OPTbIWz1bHec74pOp2PUb49x/qPXiI+LodyIs/gGajcfxywaw7nD50iITqBcvXL4+vtmKnblPnVcn6t61ypRK9kmqGoQQVVvLbuZEJPA+Jbjie4Yjb2rnaOfHuXSqUs8MuoRxjYbi/yfhCbw5/t/8vKDLzNt57Q02427Ecf4FuOJ6xmHvYudox8fJeJsBE+9+1Sm4qvxYA10U3UwCKgB+tf1VGlT5ebjZWqVueN8fZge+9N2qAb6N/RUfajqXa8hhKDsmH+1Hpz/VbShLdrx5KVKFTe5Yw9Qz03WatSHkmMOrD1AQsUE7J/YoT+Y15jZMGsDP0z9AfmAhJlAf+B3OLf7HDaLLc129qzeQ1K9JOzT7fAkmH82s/bjteluEHu7SiGVGPzhYLzbeSP8BBUPV+TFRS+6PL9qs6oMmjII00MmhJ+g8r+VGb1gdMYu1nF9qt1cFA8SuvG2TQ48j+pRKznGYXPg8EqRrEyAREvIiUAjIBxoph13OBwc23qMz0d9TkxEDDVa1WD47OE4bA7tJlwyb23Uy71Mo27VrxWt+rXCYXeg06ffb3noqYdo/WRrpENm6Hwll/Dw2rX6SVNyTFD1ICwbLTAF+B3oCb5BvjTt1RQOABOAnYA/GIoYiLwYyXth7xH+ejgJOxM4WOAg056cRoMuDdBv0iM+FPA7GHsbaflUy0xv6ZVSZpKuEEIl6bwq1fIEntO7zvBPmxBCL4TYL4T4OUNP8JzXqLjJ1bNXObzxMElx2njq80fOY2xrhP3AJKARxF+M59rZa3g94gU9gbLAHLBH2Tn02yHogrZ5uAVsH9o4tu4Y/sX8eXfzu9TbWY+yk8rSpVkXhn52a9W4uBtxhJ8Mx2ZNXTqJi0z7uKKkkjxxxj8GT0lkmSl9vAD8Dfind2LgDW3NlDQ2olbyiWl9pvHXD39pO1zFw6j5o/Dx90EXqYMfuTmFXP+JHr9AP/QX9FilcxH8C6A3asdtu2xQDm1bQwfovfTo9DqCqgXx6vJX77juyvdXsuLtFegL6/HR+/DGL29Qunpplr+3nFXvrdKOG3yY9Mskz9pfUPE8qUaGuDeRZahHLYQIRlsyZ15GGw5b5ZHvIJQcsG3pNv5a9xccByKAufDxsx/TsEtDSulKYXzYCJPA1MZEn3f6ENI9hJKOkhi7a8eNbY088d4TBJYKxBHpgGPAGWAieBfxdlmH/vuPv1k1axW24zbMZ8xEjYvi/X7vc3TzUVbPXY3thPP46Cje73+X1fkUJZmH3GjMaI96BjCem/sjZ8wdo1+SqV62x5JScui3Q1w/f52KIRUpX698pts48vsRaAckP7UvyP9J4iLjmLxuMkvGLuHq3quEvBxC+8Hahq1v//Y2vy/4nRvhN6g5pyb1OtTjtzm/4dXdC0uwcx3pgRA3NA6b1YbB684f3bMHz+Lo7IDkjvIACB8ezpkDZ3B0dUDyJi4D4fKoy3l3DWcla6WZyHL25ybdRC2E6AZckVLuFUI8dJfzBgODAcoWLZrqsVQbNjwKDh0qWXsgKSUfD/yYfX/tQ4ZI5GuSAVMG0Pbptuk/OYUKDSpo61hEoW0GvgXwggJFCzC9/3SOHj+KrCc5OvEofgF+NO/dHKOPkU7DUq/LUax8MWzv2bRVQf2B9eBT3CfNJA1QvGJxdJ/rIA5t6vdaCKgQQIlKJdAt0EE84KcdD6wUqJK0kjluHBmSkdJHC6C7EOIM8C3QVgix5PaTpJRzpZQhUsqQYv6uy9hhK/Pdmt+5xvFtx9m7fS9Ju5Mwf2nGstnCvBHzMn3zrePzHSlXsRxUABoDXaHfpH4c2XCEo38fJenPJMyLzFjWWZg1ZJbL8c/WJCvCLqAm2hrQ/cGeaHd5foPODWjWqhmmmiZ82/ji/aw3YxaNoVG3RjRp1gRTDe24zxAfxix0vbWWotyVG0aGpNujllK+ArwC4OxRj5VS9r+fiyav+f1bKEQFoHrXHiIqPApdLd2tMcrVAT0kxiRq22Jlwhs/vcFnz35GxNkIGr/YmB7jerBl0RZkXamNnwaoB9Z4K1azFaO38Y42oiOi0XfQYx9l18ZX1wZrGSt2mz3NXrUQgudnPU+XIV2IuRpD+Xrl8S+mdRqGfT6Mboe6acfrl785pV1R7kkO967dOuGl/YZcNd0+z6sUUgn783bYATQFMVMQWDqQAoULZKqdpPgkXm71MtebX8fe1c7Vz6+SkJBAh4EdkOMl7AUagJgqCKoXlGaSBqjStApMBEYC7UD3lo7gxsEuSx/J0qqrCyHuqd6uKHfVcX2O9DgzNWpfSrlZStktKwNIHrKY6p2EKom4RfEKxRnz5Rh8HvFBmAQlvizBxB8nZrqWe2DtAWJKxmCfa4cBYF5rZv2n6ylZuSTDZw/XNhowCYJWBfHqijuH2CUrV7ccQz8ZiqmNdn7ptaV5ednL9/syFSVrtd+Q7aWQbNk4IKRSJblnyr1vHHBzs2HVw3YLKSU2iw0vk1f6J6dh29JtfD7ncywWC1wGmoBYJVgcvRijtzHT7d9vPIqSY+5j3PXdNg7wyHmwYavUDUd3EkLcV1IsU6sMlt0WGAb8BviAbynfmyWOzLZ/v/EoSo7JpnHXHrso0x03HEH1sHOJc4fPYepswvyEWTvwBSQWSMSSaMHoYyQpLom4yDgCgwLRG9LZwFZRcptsGHftsYk6WfsN2v8eMpNTyQDvAt6Iy+LWbuARWq/YYDSwbs46Fo1dhM5fh4/Jh9d/ev2OdZ8VJU/IwpEhHln6SMsd7yhUScRjNejcgBKUwKunF7wLprYmwt4I4+yhsyyevBjbQRuWixaiX4vmvcfec3e4ipK9Oq6HgCjuJ2l5fI86pVQzHNUNxwyRUvL3H39z7ew1yjcoT9naZbP9mgajgXc2vMOGuRu4dukaNT+oSUj3EDYt3IQIFVDReeIAuP7cdSyJFry8vTi88TBR4VFUblw51c4wipLr3edY5FyVqFMKW6XGYGfEnJFz2L52O+IBgWOc456mhN8Lo4+RLi90SXWsWPlisAuIRVs1Ziv4FPbBYDIwrf80Dh88DHXAMdrByC9G0viRxtkep6LkmPvY+ivXlD7SkjwG++a7ClUOSeXkXyfZ/vN2zPvMJH2ThGWLNiXckmRxSzy1HqpFy04tMdU24dPRB1MvE6O/Gs3BdQc5fOQwSXuTSFqahOUXC58++2mmt9ZSlFzhHkaG5NoedUrqhmPaIi9Foqutu7XmYTUQ3oL4G/EYS6U9GzA7CSEY8skQ2j/dnqjLUZSvX57CpQvz+/zfkQ1STC0PAXOUWY2dVvKuTPauc3WP+nbqhmNqFRpUwL7LrpUbJDAf/Ar5UahEIbfGVbFhRRp2bUjh0oUBqNy4MvJXCUe0OMU0QVD9IJWklbwvVdJyLU/0qFNSNxxvKVauGKMXjmZG1xlYE6wElgvk1R9fva+9BbND2TplGfzRYOa0mIPdYqd49eK8svIVd4elKDkjZdJyIc8l6pTy0w3Hi8cvMmvkLK6euUqlRpV4buZz+Bf1p0TFEpRpWIarZ65SsVFFAkoGuDvUNLXq14qWfVpiSbDgXcDb3eEoikfxrK5VNsgPNxzjbsQxMXQiJ7ufJOrHKA4UO8Dbj7xNzLUYJrafyMmeJ4leHc2+gH283fNtj71Jp9PpVJJWlDTk6R51SnfccIQ808P+Z+c/2KvbkcO1BGyfYedi8YvsX7MfR20HPK+dZ59p53yR88RcjaFQcffWqRVFybg836O+XXIP++aiT3mAydeEvCrB7jwQDY4kB34BfsgrEhzO41EgLRKjT86P+FAU5d7lu0SdLC/tkl69ZXXKFCuD8REjTANTqIl2g9vRoEsDggODbx1vZ6L98+3xKeiTfqOKongMj1yPOqflhXKI1Wxl3ex1hJ8Np2qjqjz4xIMIIbAkWVg/ez3h58KpFlKNlv1aqk1dFcUD9Ra9Xa5HrRJ1CnkhYSuKkjvdLVHnm5uJGZE8nPHm+GtQCVtRFLdLN1ELIbyBrWgTfA3ACinlG9kdmDslb1qQlyfMmBPMrPlkDZfPXaZmk5q0fqq1KokoiofKSI/aDLSVUsYJIbyAbUKIX6WUu7I5NrfLqxNmbBYbE9pP4FLQJaytrOycuZNTh04xcPpAd4emKEoa0h31ITVxzk+9nP9y+TiJjMuLu6Qf3XyUCEsE1mVWGAHm38xsmL2BpPgkd4emKEoaMlSjFkLogb1AZeAzKeWfaZwzGBgMULZo0ayM0SOkWkPkUXDoyLU9bEuiBVFU3Poz7Q/CS2Az28DPraEpipKGDI2jllLapZT1gWCgsRCidhrnzJVShkgpQ4r5+2dxmJ4lbGXu3iW9esvq6A/rEZ8IOAiGoQYqPFABv0CVpRXFE2VqwouUMgrYDHTKjmByk7BVuXeGY8EiBXlrw1tU/bUqRfoV4QHbA7y64lV1M1FRPFRGRn0UA6xSyighhA8QCkzN9shyiTtuOEKuKImUrl6at359y91hKIqSARmpUZcCFjnr1DrgOynlz9kbVu6Ssn6dG5O2oiieLd1ELaU8BDTIgVjyhDQ3LgCVsBVFuWdqZmI2Sp44o3rZiqLcD5Woc4AqjSiKcj9Uos5hKZP2yp5g16d4UCVtRVHSoBK1Gz266tbHd/S0U1IJXFHyNZWoPYSrjYjv6HWD+xP37ePG3R2PouRxKlF7uJS9bnCRuCH7k2WK5By6AQKjXMSjkraiZDmVqHOZ2xM3pFM2yarrrgS9I43jKeJJNRwRVNJWlCyiEnUe4KpsktPCXCVtlbAV5b6oRK1kCzWGXFGyjkrUSrZyOVMTVNJWlAxSiVrJMao0oij3RiVqxS3SLI2ohK0oaVKJWnGr5NLIhnYQ459iqJ9K2opyk0rUikdQ66EoimsqUSseR92AVJTUVKJWPFqaNyBVslbyGZWolVwjOWkv75XioEraSj6gErWS6zy2Qvtf1bKV/EIlaiXXcnkDUiVsJY/RpXeCEKKMEGKTEOJvIcRRIcQLORGYomRG6Eatpx14A22lv+R/ipIHZKRHbQNelFLuE0IUBPYKIX6TUh7L5tgUJdPUMD8lL8rILuSXgcvOj2OFEH8DpQGVqN3kn0uXOHvtGjVKlya4SJF7buf4xYucv36dWsHBBBUunIURegZVGlHyikzVqIUQ5YEGwJ9pPDYYGAxQtmjRrIhNScPUFSv48IcfqGUwcMhmY+7w4TzatGmm23lz6VJm/fILNQ0GDtvtfDl6NF0bNsyGiD1DyhmQqpet5Dbp1qiTCSEKAN8Do6SUMbc/LqWcK6UMkVKGFPP3z8oYFadjFy4w44cfOGCx8HtCAustFgZ++imJFkum2tn/33/M/eUXDjvb+cls5smPPsJmt2dT5J4juZZ9Rz1bUTxYhhK1EMILLUl/LaVcmb0hKa78d+UK9QwGSjk/bwj4CcGV6OhMtXMqIoIH9HqKOT9vCugcDq7HxmZhtJ5P3YBUcot0Sx9CCAHMB/6WUn6Y/SEprtQMDmavzcYxoCbwK2DX6ykVmLl9uGqXKcMOu51/gSrAD4C3yUTRfPpOSN2AVDxdRmrULYAngcNCiAPOY69KKddkW1RKmioUL86MwYNpPmcOhfV6EoRgxcsvYzRkbjh89dKlee/ppwlZuJAiej1mvZ6Vr7yCXpfhSlie5XKdEZWwFTfKyKiPbagfU4/xRKtWdH/gAcKjoihTpAjeRuM9tdO+fn061anD6cuXaVG7NnXLlbundrYeO8agjz4iPiGB8qVL88vrrxNQoECm2zkZHs5rCxcSHhnJg3Xr8nrfvpn+A5TV1HZiiqcQUmZ9US6kUiW5Z8qULG9XyRoxCQk0GDWKJ2NiaO1wMMvLC1uNGqyaMCFT7ZwKD6feyJG8hPa2633gVKFC/PvFF5lq50p0NA1GjeKFhARCpGSa0UjJkBAWjBqVqXZygkraSnbpLXrvlVKGpPWYmkLu4ewOR5olCYvNlqkeZ6LFgo+z973l2DEqmM1McjiwAy2sVoocPUpUfDwBfn4ZbvOzdetoAkx0ft4M8I+OJjIujsKZ6FWvO3iQZjYb452dhsYWC0V27WKu3Y5Br0/n2TkrZWlkZU+10YGSM1RR0kNtPXaMCoMGYezThwYjR3L84kUAZvzyC/6PP453v34U7duXX/fvv2s7g+fMwa93b/z69yegd2++2rIFg15PuM1GWcAINAdsUma6Rm3Q60lM8bkFbdCEIZPt6HU6klJ8noSW97T72J7r0VVq1IiSM1Si9kARUVH0mjKFWTExmIEh4eF0mzyZPadO8dqiRSyXEgswyW7n8SlTsNhsabbz8969fL1xI6vRkugbwLDPPqNM4cKcsVpZBJiBPkAhLy8KeHtnKs4x3bpxTKdjCLAEaAfULVUKf1/fTLXTtWFDTvj6MlqvZwnQ1WRieIcOuebmphqbrWS33PGbkMeduXKFLceOcfnGDQD2nzlDXZ2Ozmi1qaGAJTGRJVu30gDo6Dw+HDBIyd7TpwGtF/7xmjUcOXcOgIWbNtEYLYEagNHO6323axdtvb1p4zw+FrA6HFyN0eYx/X74MB+vWcPfzl58spPh4Ww5duzmeSUDAvhz+nS2FivGOJOJIrVq8df06Zl+/YV8fdn+/vtENW3KV1Wq0CssjPeffjrT7XiC5KSts6MStpJlVI3azWb+/DOTly6lupcXx202vhgxgvLFi/Ov3U4cUAC4ANyw26kRHMxSIB7wA845P65QvDg93nqL3w8fphLwCjC+Vy8qlSzJLiAR8AH+Q+tBN6hQgYVJSSQB3sBpIN5mo5CvL53eeIMdf/9NReBVYGK/frz8yCO8uXQpn/78M1W9vDhht7N03DhC69bl+VmzuHT1KuWBbUeP8smvvzLm4Ycz/XX4cNUqft29m0oGA9POn6dZ9eq0rF79vr627pQ8YkTtSqNkBdWjdqOT4eG89e237LNa2eacEj5g5kyqlCxJl6ZNaWIyMcRopLnJxJuPP86z7dpRolgx6gDPoM1MfLhhQ/7891+2HD7MCeAA8DvamiDdGzUiDqgDPA00QvuG/33hArFAE2AI2ogNKSVLtm5l999/86+znV+Byd98w85//mHemjUcdca53Gym3/TpfLFxI4f//ZdTwEFgNTBh8WIcDkemvg5bjh1jxcaN/G2xsD0hgYVJSTzxwQf3+dX1DGGrVDlEuX+qR53FHA4Hy3bs4N/wcOqWLUuPBx5weVPsdEQEtXQ6pgDH0RKmv5Rcjopi1vPPs65FC05HRPC/8uVpXq0aAH999BEPvv46v1y7RtPKlVnx8su8tnQp9YANwBkgBDAB20+coLmXF1FWK+uBDsDvXl5sOnKEB4AX0HrlA4G2wK8HDtAYKOGMrxVaYt9x4gRNdbqbU84fApKsVnacOEFzIHkJrnaAHbgQGUnJgAAWb93KpchIWlSvTtvatV1+zf69fJkHgeRRb52Bi3FxmK1WTF5eGfzKe67kkSKqd63cK9WjzkJSSp6ZMYOP5szBvHw5E2bOZPzChS7Pr1KyJPuSktiB1rtdDNywWgkuUgQhBJ3q1+f5jh1vJmmbzUbN557Dfvo0A2NiOLxvH+1ee42W1auzH5iDNmJiJNrNwza1arHDakWP1gP/C0i02+nWqBF/oU1Dfx64CjiARxs3Zgdasgf4Ca0T2KZWLbbZ7Zx1Hv8RKOTjQ/s6ddiCluwBVgJeQlCyUCG6vPEGSxcuJHH5cgZMmcInP//s8utQu0wZNgKXnJ8vBSoFBuaJJJ2S6l0r90r1qLPQ4XPn2Lx/P8fNZnyAsWYzFTds4MVHH6VkQMAd5/9x/Dg+wC60WvFYtIW+L1y/TpVSpe44f8HmzVhjY9mFNqxuNFDm33956PRpAoDNgBdaT7kssPbAAQKBTc7jw4GyDgdRCQk4gFpAESASLcF7G40UFIL6UlICiAbsQlCrTBle69uXet98Qwm9nlidjlUvv0yTKlVYtWMH1fbupQhwA5j13HOsP3SImPPn2WU2owMGWyzU/PprhnXpkuZIjqZVqzKiZ09qrlhBCYOBBIOB1S+9dG/fBA93x3KrqnetZIBK1FkoKj6ewkLQDLgOBAH+Oh3RCQkYdDomfPUVJy9coG7lyrzZvz/hN24QjJakAQqj3Ty8GBlJIV9fJnz1FacvXqRB1apMfuIJbdo4WpIGKIZ2k/BMRATl0JIxQHG00seZq1dTHS/pPH726lUao80k3AF0R+tdn792jRBvb+YnJhIBlANK6fUkmM0M69qVPq1aEREdTflixfA1mQD4cOBA7DYb/0VE0KdBA/q2bMl3O3ZQQkqeRttxojnaxB2z1Xrzebcb9+ijPB0aytWYGCoWL37PU+Nzi9CNajMDJeNU6SMLVS5ZklNJSTQD5gHlgSiLhVKFChE6YQKm7dsZd/o0EZs20fPtt+nVtCl/A18CV4B3AZtOR71y5Wj76qsU2LGDcadPc37jRnq9+y69mzblIPC18/zJgE6v54XOndmHVjKIQBsv7aXXM6hNG3YDy5zHXwX0QjCyc2d2oY32eByYDfgYDIQ1acIOKdmE1tN+S6ejVlDQzdmKRQoWpGZw8M1kGxUfT6tXXqHO4cO8Fx7OsY0bGfjxx9QtV45NZjMVgBeBbUBJX1+XSTpZMX9/agYH5/kknSzNZVYVJQ0qUWehNfv3UxyYhTbW+RtADyzauhVddDQz7HY6AousVo6cOYNOp2PhqFGMNxgoB8wymfhl0iQOnDmDX1wc053nL7Za2X3yJP6+vswdMYJRej3lgHne3vw6eTJ1K1Rg9rBhjNDrqQAs8PZm7VtvEZ2URBUvL6ailTn2opUyyhYrxidDh/Kcs51vfHzY8O67BBctyuoJE3i7RAlqmUwcrlaNVRMmuLwZuuHwYWqYzUx2OOgErLBYWL57NwfPnqWV0chkoBPaaJDwxETMVus9f22zY00aT5GcsP1jUAlbSZMqfWQhIQQ2tN8zgXaDLnlKtS1FonEADinR6XT0bt6c3s2bp2pny7Fjd5xvd57/xIMP8sSDD95x7adat+ap1q1THVt74AA+QrDTGU8CUEIIBDCwbVsGtm17RztNq1blwMyZGXu9aDsfJ0ue46ETApmiFp18zr1MCf9pzx6GzZ5NRHw8rSpVYvG4cWnW+/OCjuu1/79/FBzJXz5VElFQPeos1bd5cxK8vHgSWA70AAr6+jKgTRt8ixVjgMHAcqC30UjT6tUp42Jj2qZVqiCKFGGw8/wwo5G2tWtTolChTMVTuWRJjlksPAd8B3QDfPV6CmVyircrHerV46xz6vd3wMNGI0+3bMnDjRpx0seHF53Hu5tMDGzVKtPLlh67cIEBM2bwTWwsNxwOGpw+Td+pU7Mkdk8WttI5uzH5L72S76lEfR8u37jBzn/+ubkVlq+3N/s++YT/ypfnFT8/kqpW5dCnn2IyGln/1lsUa9+epbVr06BrV5a99JLLHqbJy4sNb79NodBQltauTZOHH+ab8eMz3SM9cu4cTb298UGrU4ei3dS7EnPHlpf3pKCPD39MnYr9oYdYVqcO3Xr1Ytbzz+Pv68u2qVOxOI/36NWLmUOHZrr9bceP8zDQEvAF3rXb2Xb6NFYXa5vkNWEr1VR0RaNKH/do4YYNjP3ySyoZDJyy2Zg7bBhhzZoRXKQIO95//47z/X19ef+ZZzLcfoCfH9MGDLivGIv6+3NWSlajjSw5B7wjJf4+PvfVbkrFCxXikyFD7jheIiCAmWkcz4yiBQtyTKfDjlbrPwYUNBo9bunT7KSmoiugetT35ML164xduJCdFgt/JSSw0WLh2c8+Iyo+3t2hpdKiWjVC6talhbc3w728aGky8W6/fumOvvAU3UNCCChfntYmE8O9vOhgNDLz2Wc9fvnT7KAmy+RvGdncdgFaefOKlNL1POB85HREBNW9vKjqHMVQHyip13P++vVMLbwP2pTz7//8k1MRETQoX56O9etnWZxCCBa/+CI/7tnDuWvX+KZixVy10JFBr+fnN95gxa5dRERH83O1aoRUquTusNxGTUXPvzJS+vgS+BT4KntDyT0qlyzJiRS7gf8FRDgclC1aNJ1nppY85fzo/v20sVoZ4eVFv86dmdSvX5bFqtPp6Nm4cZa1l9MMej19WrRwdxgeJWyVmiyT36Rb+pBSbkWbZaw4BRUuzMeDB9PSaKSujw+djUYWjhyZ6dEU+//7j6379/OH2cwHDgfbzWY+/PlnrsfGZlPkSl5xx2QZhQvHLvBmuzfp79ufIUFDWPb6Mhz29FdyPH/0PG93eJv+vv0ZWHQgXzz3BUlxSXect/vH3bxY50We8H6C0TVHs2PZjux4GWnKspuJQojBwGAg0z3L3OiJVq3o1KAB565do3yxYgTew87bkXFxlNPrSb61VwwI1OuJio+nSMGCWRqvkjepqeiauBtxvBX6FsE1gxn/43jCT4Wz+MXFSIekz9t9XD4vITqBN9u+SamqpRi1bBRx1+NYMn4JNy7fYPwP42+ed3zbcaaHTafD8x145pNn2L9mPx/3/Ri/QD/qdaiX7a8vyxK1lHIuMBe0Xcizql1PVqRgwftKqA0qVOAE2tTvzsA8IfApUIByxYql80xFueWOhZ4g3yXs3z7/DUuihRdXvoivvy9129clMSaR5ZOW0318d3z90363u27WOiyJFl766SX8ArT7SwUKF+D9Hu9zas8pKoVo90S+f+t7arSqwYBPtJFYtdvU5sLRC6x4c0WOJGo16sONihQsyM+vv87UEiUoYzDwY7lyrJk0KV8NP1OyTn6ein7g1wPU61gvVUJu0acFlkQLx7Ycc/m8MwfOUDGk4s0kDVC3Q12EEOz7ZR8AVrOVI5uO0Kx3s1TPbd6nOf/s/IeE6IQsfjV3UuOo3axRxYoZnrKtKBlxx1T0fNC7vnj8IrXa1kp1rGjZoph8TVw6fglc7A5nTbJiMKZOg3qDHqETXPxb2zM04lQEdqud0tVLpzqvdI3SSIfk0j+XqPxA5ax7MWlIt0cthFgK7ASqCSEuCCEGZmtEiqJkibCV+WfsdfyN+FS94mR+gX7E3Yhz+bySlUty9uBZbNZbs11P7z2Nw+4gLlJ7XvLzb2+/QGCBm9fObhkZ9dFXSllKSuklpQyWUs7P9qgURckSN3dFT143JA8n7LQmQkkp7zpBqt2z7Yi5GsOCEQuICo/i/NHzzHt+Hjq9Dp3+tvR4WzPJKzrmxAQsVfpQlHwgbKX2f14th/gF+hEfdWfPNiE6Ic2edrLS1UszeO5gFo1exIY5GxA6QejgUIQQFCqhLYKW3HNOiEpdi06+nm9A1ixydjcqUStKPhK2En4LhagA54E8krBLVy/NxeMXUx27dv4a5ngzQdWD7vrctgPa0rJfS8L/Dce/uD/+Rf0ZUGQAbQdpywCXqFQCvZeei8cvUrN1zZvPu3T8EkInCKp69/azghr1oSj5TPsNznJI8sp8eUD9zvU5uO4gibGJN4/tWLYDo48xVXJ1xehtpGydsgSUCGDrkq1Ih6R5b22deC+TF7Xb1GbX8l2pnrNj2Q6qNquKb6Hs71GrRK0o+VTYqryTrNsPbY+XyYtpj07j0IZDbJi7geWTltNtTLdUQ/ZGVB7B7IGzb36eEJPAkpeWsO+XfRxYd4CvX/6aOYPm8Mwnz1Cg8K1JbGETwzi6+ShfjvqSo5uPsmT8Evav2U+v13vlyOtTpQ9FycfCVsG6DhDjT64ugxQILMDrG19n/vD5TH14Kn4BfnQd3ZXek3qnOs9hc6SaVq7T6ziz/wwbv9iIJdFC2dplGb18NI0fSb0+TvWW1RmzYgzLJixj/ez1FK9QnJHfjMyRyS4AIjv2ogupVEnumTIly9tVFCV7qBX53K+36L1XShmS1mOq9KEoSp4qg+RFKlErigJoyfrm9HPFo6hErSjKTR3Xq561J1KJWlGUVG6WQRSPoRK1oih3KJQPV+DzZGp4nqIod1D7M3oW1aNWFMWlsFXOBZ0Ut1KJWlGUuyoUjSqDuJkqfSiKcleqDOJ+qketKEqGqDKI+6hErShKhqkyiHuo0oeiKBmmyiDuoXrUiqJkmiqD5KwMJWohRCchxAkhxEkhxMvZHZSiKJ5PlUFyTkZ2IdcDnwGdgZpAXyFE+lsmKIqSp6XaOFfJVhnpUTcGTkopT0spLcC3QI/sDUtRlNziZs9ayTYZuZlYGjif4vMLQJPbTxJCDAYGOz+NE717n7j/8LJVUeCau4PIQer15m3q9eZ+5Vw9kJFEndZ93Tv+fkop5wJzMxGUWwkh9rjaTSEvUq83b1OvN2/LSOnjAlAmxefBwKXsCUdRFEW5XUYS9W6gihCighDCCPQBVmdvWIqiKEqydEsfUkqbEGI4sA7QAwuklEezPbLsl2vKNFlEvd68Tb3ePCxbdiFXFEVRso6amagoiuLhVKJWFEXxcPk2UQsh9EKI/UKIn90dS3YTQpwRQhwWQhwQQuxxdzzZTQgRIIRYIYQ4LoT4WwjRzN0xZRchRDXn9zX5X4wQYpS748pOQojRQoijQogjQoilQghvd8eU3fJtjVoIMQYIAfyllN3cHU92EkKcAUKklHltgkCahBCLgD+klPOcI5V8pZRRbg4r2zmXe7gINJFSnnV3PNlBCFEa2AbUlFImCiG+A9ZIKb90b2TZK1/2qIUQwUBXYJ67Y1GylhDCH2gFzAeQUlryQ5J2agecyqtJOgUD4COEMAC+5IN5HfkyUQMzgPFAfllORgLrhRB7nVP987KKwFVgobO0NU8I4efuoHJIH2Cpu4PITlLKi8A04BxwGYiWUq53b1TZL98laiFEN+CKlHKvu2PJQS2klA3RVkAcJoRo5e6AspEBaAjMllI2AOKBPL80r7PE0x1Y7u5YspMQIhBtUbgKQBDgJ4To796osl++S9RAC6C7s277LdBWCLHEvSFlLynlJef/V4BVaCsi5lUXgAtSyj+dn69AS9x5XWdgn5Qywt2BZLNQ4D8p5VUppRVYCTR3c0zZLt8lainlK1LKYCllebS3ir9LKfPsX2QhhJ8QomDyx0AH4Ih7o8o+Uspw4LwQoprzUDvgmBtDyil9yeNlD6dzQFMhhK8QQqB9f/92c0zZTu2ZmPeVAFZpP9MYgG+klGvdG1K2GwF87SwHnAaecXM82UoI4Qu0B4a4O5bsJqX8UwixAtgH2ID95IPp5Pl2eJ6iKEpuke9KH4qiKLmNStSKoigeTiVqRVEUD6cStaIoiodTiVpRFMXDqUStKIri4VSiVhRF8XD/BwmsYCWY+Y3mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# License: BSD 3 clause\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import (KNeighborsClassifier,\n",
    "                               NeighborhoodComponentsAnalysis)\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "n_neighbors = 1\n",
    "\n",
    "dataset = datasets.load_iris()\n",
    "X, y = dataset.data, dataset.target\n",
    "\n",
    "# we only take two features. We could avoid this ugly\n",
    "# slicing by using a two-dim dataset\n",
    "X = X[:, [0, 2]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, stratify=y, test_size=0.7, random_state=42)\n",
    "\n",
    "h = .01  # step size in the mesh\n",
    "\n",
    "# Create color maps\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "names = ['KNN', 'NCA, KNN']\n",
    "\n",
    "classifiers = [Pipeline([('scaler', StandardScaler()),\n",
    "                         ('knn', KNeighborsClassifier(n_neighbors=n_neighbors))\n",
    "                         ]),\n",
    "               Pipeline([('scaler', StandardScaler()),\n",
    "                         ('nca', NeighborhoodComponentsAnalysis()),\n",
    "                         ('knn', KNeighborsClassifier(n_neighbors=n_neighbors))\n",
    "                         ])\n",
    "               ]\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "for name, clf in zip(names, classifiers):\n",
    "\n",
    "    clf.fit(X_train, y_train)\n",
    "    score = clf.score(X_test, y_test)\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.rcParams['pcolor.shading'] ='nearest'\n",
    "    plt.figure()\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8)\n",
    "\n",
    "    # Plot also the training and testing points\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.title(\"{} (k = {})\".format(name, n_neighbors))\n",
    "    plt.text(0.9, 0.1, '{:.2f}'.format(score), size=15,\n",
    "             ha='center', va='center', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-06-08 16:33:25,975\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
     ]
    },
    {
     "data": {
      "image/png": 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jZlG48FB8fZvStGlxhgyZkeq5Uqb/+mrWbMXQodMJDByIr28LWrSowIAB77N161JnfXog0BmzeSFr17qOU/Ecagq5p1JlkMwL3Qh/Nc7xdyM6nR4hzCmOmBHC9WqJOp0eu30d4ANY0IbMaQm4SZNHadLk0Tues27dHJYseRWrNY7atbsyZsyX+Pr6u7xGy5Z9aNmyT5px3sr1Seh0alXH3EBNIfdkHderrb0y6+a7kZzTvPnjGI1rEeJ1YAkmUxjduo10eb7FkghcA/YAcUDbFJNB7nT48EYWL34Xs3k7DscNjh3zZ9as4ZmOs23b/2EyfQm8ByzCZHqKnj1dx6l4DpWoPZ0qg2ReoRjSWqnw0qUTHD++jfj4qAw1s3fvz6xbN5tr186lefz69QsABAaWYurU7bRqdZUGDX7mmWfGEhb2kst2Dx/eBDwL1ELrVb+H2ew6psOHN2GxPANUB3yx2SZz9OjvGXoNKZUsWZl3391Cy5anadhwHc8/P5X27Z/NdDtKzlOlj9wgbBVsaOecGKPKIOm6baVCKeGLL0azdesyDIZywFkmTvyJSpVC0ny6w+HghRcaERFxFghi/vxxjBo1n6ZNH2PkyIZcuXIOKMX8+eMYPfpLmjXrRfHi5Rk2zPXkkZS06dO70f6QCOAQ2n36tAUEFMdo3IzFknz+QQoWLJ7Rr0YqwcE1GDnyi3t6ruI+qkedW6h9GDPPuVLhgQNr+eOP37BYTpCQsIuEhBlMn/4/l09btmwiERFxwDngCDCbmTOfZ+nSV7hyJcF5/CjwKZ98MjTTYdWu3Q44AISi9az7UqhQoMvz27YdSLFi5zGZOmA0DsJkGsiQIR9m+rpK7qV61LlJ6EY1gzGz/GO4fPkEDkcbIPnmWw+uX++PlDLN3UPOnDkAdAaS111+BLt9AGfPHgI6AF8DkUBj7PYYAJKS4tm6dTHx8TeoUyeUypUfcBlSZORFYBBQF4gG/kdSkjb7MDExlq1bF5OYGEO9eh2pUKEB3t5+TJ26lT//XEliYiy1a48nKKjq/X5llFxE9ahzm7BVqmedGe03UKZGOXS6tWg38AC+pkSJ2i63eKpatRnwA1oyBvgKgyGA8uUbAkuAn52PPY4Q3iQlxfPSSw/y1Ve/smxZJJMmPcyuXd+7DKls2TqYTL8CXYHnEGIXQUG1SUiIYdy4ZixevJFvv73GxImd2LPnJwCMRh8efPAJOnQYqpJ0PqQSdW6kyiCZUme8jU6PPIqXVxW8vWtQsOCbjBu3xOX5YWETqFChHBAMlEGIVxg7dhHFi5dDiEbAauAD4Ge8vQvwxx9LuH69DBbLDzgcH2CxfM/8+eNdtt+kyaM8+GAbvLwq4u1djcDAuYwePZ/Nmxdy40YtLJbvcTimYbF8w/z5L2fxV0PJjVTpI7cK3ZhiBiOoUsjdPfHtA3SdN5KY6BuULFnFueQnXLr0D0uWTCY6+hohIe3p0WMMOp2OqVP/4MyZA1y9epZatdrg6+vP6tUfoNPVxW5P/lpXwWZLID7+BjZbFW59D6qQmOh6TpgQgsGDZ9Cr1zji46MoVaoKBoOR2NjkdkjRThSglWOWLn2P+PgYWrToTqdOQ9Wmr/mI6lHnZu03wGMr1PC9DAoYtJOytctiNGprMEdGXuLVV1uzd289/v13JCtXruLLL28Nqytfvj4PPNDj5sSSOnVC0eu/RtutOwKDYTR163aibt32GAyLgT+ACLy8xlCvXud04ylcuDRlytTCYNBGfNSv3wEvr/nATiAcL68XadCgE5cv/8vEie3Zv78l//zzHN98M4fvv5+apV8bxbOpRJ0XhK1KMTFGJey76rjeuVmDZPfuH7BaOyDleKArZvNyNm6c6/KpFSo04IUXviAgYBAmU03q17cxYsRcKlZsxMiRnxMQ8IzzuGT48M8zHVq1as157rkZFCrUH5OpNiEhBRg6dCbbtn2L1foU2pI73TGbF7N2res4lbxHlT7yivYbtP9vjrdOpt4e3yFsJXz/KEIIhEg5i9Gebjnh8OHfiYq6DJg5fnwnsbHX8fX1p3HjnpleNzotLVo8TosWqdd/1mK6PU7Vx8pP1Hc7rwndqJVDHltxs+eopKFQNI0b98Ro3IwQk4DlmEw96dhxmMunbN68iLVrvwQ2AdeJi3uIV15pl+2hPvhgP0ympQgxFViGydSPhx92FadEvbPKe1SizsvCVoI+uX6tfnlTCd1IQAUjU6b8QYsWF6ld+xv69RtA//5vu3zKjh3LgP8BD6CNyZ5OXNzFm4+Hh5/k5MndJCXFu2jhFiklly//y8mTuzGbE+56bokSFXnnnc00bXqcOnW+Y8CAl3j44VHJLaX+9+hKdd8iD1Klj7zu0VW3Pr45WQZUSQQI3UhxGcrIkXPJyNcjIKAEcIhbU7+PIoQJKSWffz6c7du/R68vhcEQyeTJvxIcXDPNdqSUfPrpEP788yf0+hIYjdFMnrzuruOjg4NrMHr0wuQWbv0fugECo+58QqplB8jQ61M8l+pR5ydhq7Te1s0x2KrHRfsN2rsOffJa1q6/Jk899RFeXseAB4EhQBd69BjOX3+tYseO7Vgs/5KYuJ/Y2Ff58EPXm+Hu3Pkdf/21D4vlJImJB4iJeZEZMwalE6gzNp1NS87J5a20knSy5DKYWoEx11M96vwoedGiXHDjMS4umunTexIVdZkHHniEfv20XVNsNgvbt39LdPQVatR4kCpVmmjHLTa2f7ud6CvR1GxVk8qNK6d/kTTedVgsSWzf/i1xcZHUqtWGihUbUqBAAHPm/MPXX48nJuYKLVrMp3nz3qxc+S5WawegoLORx4iIcD3h5cKFvzGbOwN+AEjZi/DwyWmceSu5JjX9ie3r1pAYk0jdi3UpG1g2/deVrP2GXPG9VlxTiTo/S07Y4JFvk+Piohk4sDxSVgNCuXjxc44d28GkSRuYOLEjFy4IbLZ66PU9GTRoKi0fCmRC9wlcNFzEVseGvoeewdMG0+qJVhm/aNgqLEkWXq35MRGXArHZqqPXd2HYsE9p1qwXBQoEMGRI6qFxJUpUQsoJwAS02vVSChUq4/ISwcE1MZmmYDaPBwoixDJKlUoukziTs84ObTdBYBSJsYmMbzCJqMs1sFnL8+2Edxj3w/PU65CJndFdfq/BU77fimsqUSuam0uDek4d+7PPnkLKcsAOtCrdUP75pxF//rmSixdtmM1bAB12+wAWLGiDV6NnucQlzL+ZQQf2p+3M7zg/c4ka2PHtDiKuFMRsXgsI7Pa+zJv3OM2a9Urz/ISEKIQQSFkJKAncICnJdVWxWbPHOHBgM9u3V8JgKIHJlMCoUb/iqua8cd5GIi82xJq0AgC7tRtfDB3Bp6czkahTSpm0U32/wd3fcyVtGUrUQogzQCzaYE6blDLthXyV3C/MWQbI4V7X9esX+PrryVy/Hk69eg/yyCMvEh19BajJrVspVQErUVHhOBzVUhyvhtkcTcz1aOyl7fCED1z0gk7xJEUmuVwlz5XY67FYLUFoezhHAi1JSLjGrVJE6rbi428APYBhwA2gCBZLfZftCyHo3n0EN5JOExN/ldb9H6TkiAMgDqYdz7V4rEm1UhypQXxUbIZfz12FubrZDCppe47M9KjbSCmvpX+akie47HVl/S9vXFwkL73Ukri4/jgcXTl9egZXrpyjTZunOXnyRbRxyw2BiQhRiHr1OrB06TvAZqA+ev0bVK7clnK1KmB9yQDiRXA0hJ2TCAiKQCBc30tL4+WUrVMWh/Uj4BW0XVjeIDC4mHZjLo0NHGrXbovB0B2LpRdQFYNhDLVrd3D5eq9cOcNrbzch6aU4ZCXJpTePkmC/Tq9X0u6x1+tQh19mfI4loQdQDi/TOOp1rJvelzXzXCZtlbDdTZU+lPSl2cvOul/e/ft/xWKpj8OhjWE2mx9i8+aSPPtsHMePb+OPP7oDZnS6gkye/BPBwTUYM2Yhs2cPID7+CtWqtWH06K/Z89OPGPRdsdne0hq2NSfmYkXk8l5p96g7rHNu25Xa5X8uo/d6BLt1gvNII2Ku1tE+TLUYltZm5cqNef75T5g37zGSkm5Qp04nRo6c5/L17tixDEvvJOR47a+HuaaZNR3XuEzUNVvXZNBnYSwa0wlLYiINOocwdJ7rUSVZwk3vrJS0ZTRRS2C9EEICc6SUdyw0IIQYDAwGKFq2aNZFqHiOLBgtcujQBmbNGkZsbDiVK7dk9OgFSCmRMu02RoxYzIgRdx5v2LArX3zRNdUxKSU6XcofaUFyft6z5yfmzh1NfPxVatRox6hR825uC5AW6Uj5mSDF1t3aKIp1HSCm0M1DzZv3pnnz3ndp8TYi9cdS3n34nDCArmASQloQBRIzfp37ddebkKASd87I6DjqFlLKhmjbXgwTQtxxd0ZKOVdKGSKlDPEv5nobeyUPSDVNPeXMx7sLDz/FBx/0JTLyE6zW//jnnxpMmfI4DRp0xmjch073BrAao7EnDz74DHp95t7whYR0x2j8AyHeAlZjMj1K27aDOX/+CDNmDCQqaj5W62mOHSvO9OlPu2xHp9fhsK9C2637R+BhJPrUJ+nvfeZf8+a9MS7zhmkCfgBTHxNdhnZxef6xrcf44qUviP0+FssJC/uS9jHnhTn3dO37kvL7nsnvvXJ/MpSopZSXnP9fAVYBjbMzKCUXSZ5Eo0t/qvrx49sQogPQESiM3T6VM2d2YjL5MXXqNpo2PU/Vqp/zyCMdGTp0ZrqXjoy8yJkzB25O2S5UqDhTpvxB/fqHKFfuA3r06MqAAdM4enQzUoYBrYEi2Gwf8vfxtUj/aACunb/GmQNnsCRaANi/Zj+IHsDfwFygD9bEuNQXD93oXEsl84oXr8A7E3YR8ldTqs+rzhNDn6DXq7fKHlfOXOHsobNYzVYADv52EMsgC4QAxcH6vpUD6w7c07WzVJoTqFTSzg7pdlmEEH6ATkoZ6/y4A/Bmtkem5C5p3ohK/ba4QIFA4F+0wUN64DQ6nRGDwUjRomUYNWpBhi+39PvX+HntdAwlTegjDbw+biPly9fnt98WcuTI7xgMpVizZhYPPNAVP79AdLp/uDX1+zjefoUQQrBozLesn/07BmNxDMZoJm0ej38xf9BdAPtW5/l/gs50ZxD+Malq1ZkRHFyT8UtfBP2tZC+lZPaw2WxfsR19UT0+Vh/eXPcmBQML4vWXF1a0xM1x8Cvsl+lrZhs1RjvbZaRHXQLYJoQ4CPwF/CKlXJu9YSm52s2eViQpe1kNGnShXLlATKZQ9PqxGI1tefrp6eh0mVvJ4NixLazZ+THWf80kHo8h7qNI3v+sB0eO/M7atd9gtZ4gMfEIcXHv8MEHT9Cs2WOUKmXGZOqEXv8iRmNXBrw/iAPrDrBh7gGsSadIjDlB7LW3mdZzNk9++CRexsOgaw26EUAHuoxsfWcg7Tfcc686rZ7nzu92svPPnVhPWUk6lkTUkCg+fvZj2g5qS+DRQIw9jehH6TE+aWTQ1PSmnLtJyvKI6mlnmXR71FLK08A9jqxX8rXbJtHo9XreeOMntm//lhs3LlO9+lKqV2+Z6WbPnz+KbO+AYs4Dj8O1p85z7twRHI72QPLN7D5cvToAg8HIhAmr+eijx4iJWU/z5sNoNagGP3+8Gru1M1DYeX5fIk4/T4GAAnx66n0+evwjYq+touUTnQl7LSztYO6xV21p8Qu7vtlMfFQ8ddrVIbhmMOeOnsPczXxzJrrsK7k47SK+/r58sOMDtn2zjcTYROptrEe5uuUASIpPYteKXSTFJlG3fV2CqgVlKo5slYNDPPM6NTxPyX4pyiKG73vSuvWTzs/u7Re2dOnqiK902tySQOAHKFw6iODgGuh0nwFRQACwisKFq2A2JzLslbKYy8dDM1i27Ahx47tSp20d9F4rsVmigULA9xQrVwar2cqUx6dwyXAJx0MOVn28irK1y/JAjwfuDCaNESDpMZsTeOXh4VwtEo6jogPxpmDskrGUrlYa0ycmzC+ZwRfESkHJaiUB8CnoQ/sh7VO1kxSXxPiW47kRdANHGQe6yTpe+u4larepfQ9f1WyWsjS2sifYVdLODLV6npKz0rz5mDm1a7elXYNn8arsjW/9Qvg+F8DY51ZRp04obdr0wMurKr6+D+DrO5px45bw9dcvaUl6F/AFsAF+mfMLDbo0oNWT1fDyqYSPfz38Asfy4vdD2bFsB5eMlzBvNGOdZcWy0nL3URaZHAGyZcsirgRdxrzOjPVzK5YlFuaOmUuLvi1oVKsRxipGfBr64P+hPy988YLLdjbM3cD1qtcx/2LGOseKeZ6ZL8Z+keE43ObRjN+AVjSqR624h8sJFZCRXtbTfT+iU5vhxMRcITi4Jr6+Wo92wID36dJlCDExV53H/Vm58h2oBDwPXEVbpTRRu3n37Own6TYmlNjrsZSpVQafgj4c3ngYW23brTDqQMK1uyzuH7qR8M/q8t2PbxGTcIUmdcIIbTMYIQTh4Sf57qdJxCRcoVmd3rRtM5DomCtY61hTtR9/LR6dTscL81/g0olLJEQnUKZ2Gbz9vF1eNupq1B3txF7LoqnlOcFlLxtUTzs1lagV90pZx4RMJe6SJStRsmSldI5L6vcrw1+vAqOANsC7oA/Q37yJWapKKUpVKXXz+TVb10TfTY+9vx1qgv4VPdXbVXf5EiIvRvLK5CYkDItFVnNw4q0dREWH07bVQF6eHELiyFhkVQcnJu8gKjac2rVb8+McLyyPW6ASGF4zUKudtpaHEILS1Uu7/nqlULddXdYOXIvlUQuUBa8JXtRtlw1Ty3NCyqVm1ciRO6hErXiWtBJ3jP9tvS3I2C+vc2uqBUnoOulwvOMcodESRHXhcrGmSiGVGDpjKPO6zyMpMonq7aszZtEYl1fZuXwn5u7xyDe09s0N4/ml+Ud4GwpgeSQR+brzeL14fn7oQ8I+e4ABZQawqMMizNFmanWtxfAFwzPwelKrG1qXp157iiUPLcEaZ6Vuj7oM/WRoptvxOGp1vzuoRJ1HnNpzik/6LyTyQgTl6lVi9LJnKRJcJNuve3zbcT4d9inRF6Op3Lwyo+aNolBx1zfWjm05xmfDPyM2PJaqLasy8ouR+Bf158imI8weOVs7/mBVXvjiBQoWKXhn4gbnzTt/0vqlXbXqPZb9OhGH2Y5/kD/vNX4P6ZAY/AxY0Ca04MXNkui+NfuY++JcEq4lUDu0NsM/H45vIV9a9mlJyz4t70jme37aw7xx80iITKBux7oMmzUM6ZBam8m8QEqH9s9Lpj7u0JJ226fb0vbptple2e92HQZ3oMPgDvfdjsdSq/sB6mZinhB9JZo3233A5ROTMMef4OSfXXiz3XQcjnsd45sx185d452e73Bl8hXMR8wcr3Kcd3u96/L8K/9d4b1e73H13askHU7iaJmjTO0zlfBT4UztPZWrU5zHg47yfr/3XV+44/oUY3Rv2bdvDUtXv4rjWzucgpgHYxjfejyNHm6E4TcD4gMBa8HUy8RDgx7i/NHzfPi/D4mcGUnSoSQOeB9gxsAZqdpMmfzOHDjDjIEziJwdSdKBJPbL/cwcMpPGjzbGa6UXzADWgOkxX0IfGkKTJmF4LTPBDKEd7+1L+6c7ploIKquSa55M0rdLvhGdD6evqx51HnDyr5NoQ937AuCwv8m1c7OICo+icFDhuz73fhzfdhzxkIBHtM/tH9g5V+AcSXFJeBfwJuZaDDFXYiheoThGHyPHth5DtBfwsPP8j+yc8j3FoQ2HoBPgXGPJPsPOP77/YLPYMBhd/IiGbryjlrllyyIIQ5uhDjAL4gvEE1AygHe3vMviSYuJWh9Foy6NeHTco/w681ccvR0Qqp1u+9jG4ZKHb7YXFR5F3I04SlQsgZfJi0O/HcLez67VuQHrDCsHKx+k+DfFefv3t1ny5hJifomhaZ1BPNxpPDqdjrdf28HCpS8QE3eNlo360ePlBoCWqCMvRZIQnUDJSiVdv04lbflsdT/105EH+AX44XCcB6xo78Gv4LAn4lPQJ1uv6xvgC/9xa0b4Be24l7cXq2esZtmkZehL6NHH6Zm4eiJ+AX7a+Q6093JnQeelw7+IP+K0uHX8DOhNevRet9elb5NcFvktFEL24L85GvZxa6b4KcAEOp2OoKpBvPTNS6me7hfgh/6UHpt0jvA4CaYAbar44gmLWfvZWvTF9HjbvZm0ZhK+Ab7ot+qxS/vN9r0DtFEZZWqV4ZVlr9yKJ0qHlJINf8zlxPEdGAp7sW7TpzSPfJXihYsy/8X5bPpyE/rCevwMfkz+dTLFKxS/929GfpVPpq+r0kceULV5Vao3L4bJrxVCvILJrzk9xnfP9kRdr0M9yhYui6mDCfGqwNjaSN93+3L24Fm+++A7rEesJJ1IIn5GPO899h71O9cn2CcYUyftfFNbE/2n9iekewhBXkEYOxu1dtoZefL9JzP+dr69tn3V4+88jtdJL62H/BLQGlr3TWPqt1Pzx5tTLLIYxh5GxCsCYzcjz7z/DAfWHWD98vVYT1pJ+ieJ6DHRTPvfNFr2a0mRi0Uw9jQiXhYYHzHyzJRn7mxYaG/Jd+/+kU3HF2I7YybpvzhuvHCZDwdN569Vf7Flwxasp60knUwickAkMwbNuIfvgJJKyunr+rw1Rluktw7uvagUUklO2TMly9tVXHPYHfzx9R9cPXuVio0q0rBLwxy5rs1qY+virURejKRqs6rUDa3L5kWbWfDbApKWJGknSdD56vjy6pcYjAa2fLWFG5dvUL1FdWq31WbRWc1Wtny1hajwKKq3rH7Ps+viIuNYMGIBURFRNH6kMZ2Gd7rr+fHR8SwZu4SoiCia9W5Gq/6tWP3BapZeXor9Q7t2UizoS+hZmrCUuKg4loxdQvTVaFr2bUmLPi3Sbvj7nqz47l2WV5yEfM/5O3YFjNWNdB/ZnRX2FeDc34Bw8K7jzVdXv7qn16ykI5eM0e7dW+x1tc2hKn3kETq9jtZPue49ZheDl4G2A9qmOlaqSinkJAnXgSLARq1EYPIzIYSg3aB2d7TjZfIi9NnQ+46nQOECjPx6ZIbOtVltvBv2LucTz+Oo4eDwmMP4+vtSqmopDEsM2OPsUAD4CYpWKYrNYuPtR97mkv0SjqoODo84jG8hXxp0bnBn42GrKLUnGONaX8wT48EXWC0oXqU4QVWDtKnir5rBR2u/eBVV9sg2Lsdoe2bCTotK1EqWq9a8Gh36d2BdzXUYqhhw/ONg3HfjPG5kws7vdnLOeg7zH9qu5WyD2U/MZt6ZeTT5tQm7qu/CUNYAZ2DML2PY9s02LhouYv7def7vMHvIbOb+e8eGRwA0e8ePv3Z3Zl+VNehKeaG/7GDU2lEE1wrmz7V/cqDaAXSldegv6Hlhreup4koWSq5p32WIpydSiVrJFk++9STtnmxH1OUogmsF419U2/Un5moM3737HdfCr1G3ZV06PdcJnU7H+aPn+ejpj4iJiaFGgxq8sOQFDAbXP543Lt9g+ZTlRF6JpMFDDegwuEOm/xBEX4nGXtd+605NfYiPiEcIwfDPh9PjaA9ir8dSrm45/AL8OPTbIWz1bHec74pOp2PUb49x/qPXiI+LodyIs/gGajcfxywaw7nD50iITqBcvXL4+vtmKnblPnVcn6t61ypRK9kmqGoQQVVvLbuZEJPA+Jbjie4Yjb2rnaOfHuXSqUs8MuoRxjYbi/yfhCbw5/t/8vKDLzNt57Q02427Ecf4FuOJ6xmHvYudox8fJeJsBE+9+1Sm4qvxYA10U3UwCKgB+tf1VGlT5ebjZWqVueN8fZge+9N2qAb6N/RUfajqXa8hhKDsmH+1Hpz/VbShLdrx5KVKFTe5Yw9Qz03WatSHkmMOrD1AQsUE7J/YoT+Y15jZMGsDP0z9AfmAhJlAf+B3OLf7HDaLLc129qzeQ1K9JOzT7fAkmH82s/bjteluEHu7SiGVGPzhYLzbeSP8BBUPV+TFRS+6PL9qs6oMmjII00MmhJ+g8r+VGb1gdMYu1nF9qt1cFA8SuvG2TQ48j+pRKznGYXPg8EqRrEyAREvIiUAjIBxoph13OBwc23qMz0d9TkxEDDVa1WD47OE4bA7tJlwyb23Uy71Mo27VrxWt+rXCYXeg06ffb3noqYdo/WRrpENm6Hwll/Dw2rX6SVNyTFD1ICwbLTAF+B3oCb5BvjTt1RQOABOAnYA/GIoYiLwYyXth7xH+ejgJOxM4WOAg056cRoMuDdBv0iM+FPA7GHsbaflUy0xv6ZVSZpKuEEIl6bwq1fIEntO7zvBPmxBCL4TYL4T4OUNP8JzXqLjJ1bNXObzxMElx2njq80fOY2xrhP3AJKARxF+M59rZa3g94gU9gbLAHLBH2Tn02yHogrZ5uAVsH9o4tu4Y/sX8eXfzu9TbWY+yk8rSpVkXhn52a9W4uBtxhJ8Mx2ZNXTqJi0z7uKKkkjxxxj8GT0lkmSl9vAD8Dfind2LgDW3NlDQ2olbyiWl9pvHXD39pO1zFw6j5o/Dx90EXqYMfuTmFXP+JHr9AP/QX9FilcxH8C6A3asdtu2xQDm1bQwfovfTo9DqCqgXx6vJX77juyvdXsuLtFegL6/HR+/DGL29Qunpplr+3nFXvrdKOG3yY9Mskz9pfUPE8qUaGuDeRZahHLYQIRlsyZ15GGw5b5ZHvIJQcsG3pNv5a9xccByKAufDxsx/TsEtDSulKYXzYCJPA1MZEn3f6ENI9hJKOkhi7a8eNbY088d4TBJYKxBHpgGPAGWAieBfxdlmH/vuPv1k1axW24zbMZ8xEjYvi/X7vc3TzUVbPXY3thPP46Cje73+X1fkUJZmH3GjMaI96BjCem/sjZ8wdo1+SqV62x5JScui3Q1w/f52KIRUpX698pts48vsRaAckP7UvyP9J4iLjmLxuMkvGLuHq3quEvBxC+8Hahq1v//Y2vy/4nRvhN6g5pyb1OtTjtzm/4dXdC0uwcx3pgRA3NA6b1YbB684f3bMHz+Lo7IDkjvIACB8ezpkDZ3B0dUDyJi4D4fKoy3l3DWcla6WZyHL25ybdRC2E6AZckVLuFUI8dJfzBgODAcoWLZrqsVQbNjwKDh0qWXsgKSUfD/yYfX/tQ4ZI5GuSAVMG0Pbptuk/OYUKDSpo61hEoW0GvgXwggJFCzC9/3SOHj+KrCc5OvEofgF+NO/dHKOPkU7DUq/LUax8MWzv2bRVQf2B9eBT3CfNJA1QvGJxdJ/rIA5t6vdaCKgQQIlKJdAt0EE84KcdD6wUqJK0kjluHBmSkdJHC6C7EOIM8C3QVgix5PaTpJRzpZQhUsqQYv6uy9hhK/Pdmt+5xvFtx9m7fS9Ju5Mwf2nGstnCvBHzMn3zrePzHSlXsRxUABoDXaHfpH4c2XCEo38fJenPJMyLzFjWWZg1ZJbL8c/WJCvCLqAm2hrQ/cGeaHd5foPODWjWqhmmmiZ82/ji/aw3YxaNoVG3RjRp1gRTDe24zxAfxix0vbWWotyVG0aGpNujllK+ArwC4OxRj5VS9r+fiyav+f1bKEQFoHrXHiIqPApdLd2tMcrVAT0kxiRq22Jlwhs/vcFnz35GxNkIGr/YmB7jerBl0RZkXamNnwaoB9Z4K1azFaO38Y42oiOi0XfQYx9l18ZX1wZrGSt2mz3NXrUQgudnPU+XIV2IuRpD+Xrl8S+mdRqGfT6Mboe6acfrl785pV1R7kkO967dOuGl/YZcNd0+z6sUUgn783bYATQFMVMQWDqQAoULZKqdpPgkXm71MtebX8fe1c7Vz6+SkJBAh4EdkOMl7AUagJgqCKoXlGaSBqjStApMBEYC7UD3lo7gxsEuSx/J0qqrCyHuqd6uKHfVcX2O9DgzNWpfSrlZStktKwNIHrKY6p2EKom4RfEKxRnz5Rh8HvFBmAQlvizBxB8nZrqWe2DtAWJKxmCfa4cBYF5rZv2n6ylZuSTDZw/XNhowCYJWBfHqijuH2CUrV7ccQz8ZiqmNdn7ptaV5ednL9/syFSVrtd+Q7aWQbNk4IKRSJblnyr1vHHBzs2HVw3YLKSU2iw0vk1f6J6dh29JtfD7ncywWC1wGmoBYJVgcvRijtzHT7d9vPIqSY+5j3PXdNg7wyHmwYavUDUd3EkLcV1IsU6sMlt0WGAb8BviAbynfmyWOzLZ/v/EoSo7JpnHXHrso0x03HEH1sHOJc4fPYepswvyEWTvwBSQWSMSSaMHoYyQpLom4yDgCgwLRG9LZwFZRcptsGHftsYk6WfsN2v8eMpNTyQDvAt6Iy+LWbuARWq/YYDSwbs46Fo1dhM5fh4/Jh9d/ev2OdZ8VJU/IwpEhHln6SMsd7yhUScRjNejcgBKUwKunF7wLprYmwt4I4+yhsyyevBjbQRuWixaiX4vmvcfec3e4ipK9Oq6HgCjuJ2l5fI86pVQzHNUNxwyRUvL3H39z7ew1yjcoT9naZbP9mgajgXc2vMOGuRu4dukaNT+oSUj3EDYt3IQIFVDReeIAuP7cdSyJFry8vTi88TBR4VFUblw51c4wipLr3edY5FyVqFMKW6XGYGfEnJFz2L52O+IBgWOc456mhN8Lo4+RLi90SXWsWPlisAuIRVs1Ziv4FPbBYDIwrf80Dh88DHXAMdrByC9G0viRxtkep6LkmPvY+ivXlD7SkjwG++a7ClUOSeXkXyfZ/vN2zPvMJH2ThGWLNiXckmRxSzy1HqpFy04tMdU24dPRB1MvE6O/Gs3BdQc5fOQwSXuTSFqahOUXC58++2mmt9ZSlFzhHkaG5NoedUrqhmPaIi9Foqutu7XmYTUQ3oL4G/EYS6U9GzA7CSEY8skQ2j/dnqjLUZSvX57CpQvz+/zfkQ1STC0PAXOUWY2dVvKuTPauc3WP+nbqhmNqFRpUwL7LrpUbJDAf/Ar5UahEIbfGVbFhRRp2bUjh0oUBqNy4MvJXCUe0OMU0QVD9IJWklbwvVdJyLU/0qFNSNxxvKVauGKMXjmZG1xlYE6wElgvk1R9fva+9BbND2TplGfzRYOa0mIPdYqd49eK8svIVd4elKDkjZdJyIc8l6pTy0w3Hi8cvMmvkLK6euUqlRpV4buZz+Bf1p0TFEpRpWIarZ65SsVFFAkoGuDvUNLXq14qWfVpiSbDgXcDb3eEoikfxrK5VNsgPNxzjbsQxMXQiJ7ufJOrHKA4UO8Dbj7xNzLUYJrafyMmeJ4leHc2+gH283fNtj71Jp9PpVJJWlDTk6R51SnfccIQ808P+Z+c/2KvbkcO1BGyfYedi8YvsX7MfR20HPK+dZ59p53yR88RcjaFQcffWqRVFybg836O+XXIP++aiT3mAydeEvCrB7jwQDY4kB34BfsgrEhzO41EgLRKjT86P+FAU5d7lu0SdLC/tkl69ZXXKFCuD8REjTANTqIl2g9vRoEsDggODbx1vZ6L98+3xKeiTfqOKongMj1yPOqflhXKI1Wxl3ex1hJ8Np2qjqjz4xIMIIbAkWVg/ez3h58KpFlKNlv1aqk1dFcUD9Ra9Xa5HrRJ1CnkhYSuKkjvdLVHnm5uJGZE8nPHm+GtQCVtRFLdLN1ELIbyBrWgTfA3ACinlG9kdmDslb1qQlyfMmBPMrPlkDZfPXaZmk5q0fqq1KokoiofKSI/aDLSVUsYJIbyAbUKIX6WUu7I5NrfLqxNmbBYbE9pP4FLQJaytrOycuZNTh04xcPpAd4emKEoa0h31ITVxzk+9nP9y+TiJjMuLu6Qf3XyUCEsE1mVWGAHm38xsmL2BpPgkd4emKEoaMlSjFkLogb1AZeAzKeWfaZwzGBgMULZo0ayM0SOkWkPkUXDoyLU9bEuiBVFU3Poz7Q/CS2Az28DPraEpipKGDI2jllLapZT1gWCgsRCidhrnzJVShkgpQ4r5+2dxmJ4lbGXu3iW9esvq6A/rEZ8IOAiGoQYqPFABv0CVpRXFE2VqwouUMgrYDHTKjmByk7BVuXeGY8EiBXlrw1tU/bUqRfoV4QHbA7y64lV1M1FRPFRGRn0UA6xSyighhA8QCkzN9shyiTtuOEKuKImUrl6at359y91hKIqSARmpUZcCFjnr1DrgOynlz9kbVu6Ssn6dG5O2oiieLd1ELaU8BDTIgVjyhDQ3LgCVsBVFuWdqZmI2Sp44o3rZiqLcD5Woc4AqjSiKcj9Uos5hKZP2yp5g16d4UCVtRVHSoBK1Gz266tbHd/S0U1IJXFHyNZWoPYSrjYjv6HWD+xP37ePG3R2PouRxKlF7uJS9bnCRuCH7k2WK5By6AQKjXMSjkraiZDmVqHOZ2xM3pFM2yarrrgS9I43jKeJJNRwRVNJWlCyiEnUe4KpsktPCXCVtlbAV5b6oRK1kCzWGXFGyjkrUSrZyOVMTVNJWlAxSiVrJMao0oij3RiVqxS3SLI2ohK0oaVKJWnGr5NLIhnYQ459iqJ9K2opyk0rUikdQ66EoimsqUSseR92AVJTUVKJWPFqaNyBVslbyGZWolVwjOWkv75XioEraSj6gErWS6zy2Qvtf1bKV/EIlaiXXcnkDUiVsJY/RpXeCEKKMEGKTEOJvIcRRIcQLORGYomRG6Eatpx14A22lv+R/ipIHZKRHbQNelFLuE0IUBPYKIX6TUh7L5tgUJdPUMD8lL8rILuSXgcvOj2OFEH8DpQGVqN3kn0uXOHvtGjVKlya4SJF7buf4xYucv36dWsHBBBUunIURegZVGlHyikzVqIUQ5YEGwJ9pPDYYGAxQtmjRrIhNScPUFSv48IcfqGUwcMhmY+7w4TzatGmm23lz6VJm/fILNQ0GDtvtfDl6NF0bNsyGiD1DyhmQqpet5Dbp1qiTCSEKAN8Do6SUMbc/LqWcK6UMkVKGFPP3z8oYFadjFy4w44cfOGCx8HtCAustFgZ++imJFkum2tn/33/M/eUXDjvb+cls5smPPsJmt2dT5J4juZZ9Rz1bUTxYhhK1EMILLUl/LaVcmb0hKa78d+UK9QwGSjk/bwj4CcGV6OhMtXMqIoIH9HqKOT9vCugcDq7HxmZhtJ5P3YBUcot0Sx9CCAHMB/6WUn6Y/SEprtQMDmavzcYxoCbwK2DX6ykVmLl9uGqXKcMOu51/gSrAD4C3yUTRfPpOSN2AVDxdRmrULYAngcNCiAPOY69KKddkW1RKmioUL86MwYNpPmcOhfV6EoRgxcsvYzRkbjh89dKlee/ppwlZuJAiej1mvZ6Vr7yCXpfhSlie5XKdEZWwFTfKyKiPbagfU4/xRKtWdH/gAcKjoihTpAjeRuM9tdO+fn061anD6cuXaVG7NnXLlbundrYeO8agjz4iPiGB8qVL88vrrxNQoECm2zkZHs5rCxcSHhnJg3Xr8nrfvpn+A5TV1HZiiqcQUmZ9US6kUiW5Z8qULG9XyRoxCQk0GDWKJ2NiaO1wMMvLC1uNGqyaMCFT7ZwKD6feyJG8hPa2633gVKFC/PvFF5lq50p0NA1GjeKFhARCpGSa0UjJkBAWjBqVqXZygkraSnbpLXrvlVKGpPWYmkLu4ewOR5olCYvNlqkeZ6LFgo+z973l2DEqmM1McjiwAy2sVoocPUpUfDwBfn4ZbvOzdetoAkx0ft4M8I+OJjIujsKZ6FWvO3iQZjYb452dhsYWC0V27WKu3Y5Br0/n2TkrZWlkZU+10YGSM1RR0kNtPXaMCoMGYezThwYjR3L84kUAZvzyC/6PP453v34U7duXX/fvv2s7g+fMwa93b/z69yegd2++2rIFg15PuM1GWcAINAdsUma6Rm3Q60lM8bkFbdCEIZPt6HU6klJ8noSW97T72J7r0VVq1IiSM1Si9kARUVH0mjKFWTExmIEh4eF0mzyZPadO8dqiRSyXEgswyW7n8SlTsNhsabbz8969fL1xI6vRkugbwLDPPqNM4cKcsVpZBJiBPkAhLy8KeHtnKs4x3bpxTKdjCLAEaAfULVUKf1/fTLXTtWFDTvj6MlqvZwnQ1WRieIcOuebmphqbrWS33PGbkMeduXKFLceOcfnGDQD2nzlDXZ2Ozmi1qaGAJTGRJVu30gDo6Dw+HDBIyd7TpwGtF/7xmjUcOXcOgIWbNtEYLYEagNHO6323axdtvb1p4zw+FrA6HFyN0eYx/X74MB+vWcPfzl58spPh4Ww5duzmeSUDAvhz+nS2FivGOJOJIrVq8df06Zl+/YV8fdn+/vtENW3KV1Wq0CssjPeffjrT7XiC5KSts6MStpJlVI3azWb+/DOTly6lupcXx202vhgxgvLFi/Ov3U4cUAC4ANyw26kRHMxSIB7wA845P65QvDg93nqL3w8fphLwCjC+Vy8qlSzJLiAR8AH+Q+tBN6hQgYVJSSQB3sBpIN5mo5CvL53eeIMdf/9NReBVYGK/frz8yCO8uXQpn/78M1W9vDhht7N03DhC69bl+VmzuHT1KuWBbUeP8smvvzLm4Ycz/XX4cNUqft29m0oGA9POn6dZ9eq0rF79vr627pQ8YkTtSqNkBdWjdqOT4eG89e237LNa2eacEj5g5kyqlCxJl6ZNaWIyMcRopLnJxJuPP86z7dpRolgx6gDPoM1MfLhhQ/7891+2HD7MCeAA8DvamiDdGzUiDqgDPA00QvuG/33hArFAE2AI2ogNKSVLtm5l999/86+znV+Byd98w85//mHemjUcdca53Gym3/TpfLFxI4f//ZdTwEFgNTBh8WIcDkemvg5bjh1jxcaN/G2xsD0hgYVJSTzxwQf3+dX1DGGrVDlEuX+qR53FHA4Hy3bs4N/wcOqWLUuPBx5weVPsdEQEtXQ6pgDH0RKmv5Rcjopi1vPPs65FC05HRPC/8uVpXq0aAH999BEPvv46v1y7RtPKlVnx8su8tnQp9YANwBkgBDAB20+coLmXF1FWK+uBDsDvXl5sOnKEB4AX0HrlA4G2wK8HDtAYKOGMrxVaYt9x4gRNdbqbU84fApKsVnacOEFzIHkJrnaAHbgQGUnJgAAWb93KpchIWlSvTtvatV1+zf69fJkHgeRRb52Bi3FxmK1WTF5eGfzKe67kkSKqd63cK9WjzkJSSp6ZMYOP5szBvHw5E2bOZPzChS7Pr1KyJPuSktiB1rtdDNywWgkuUgQhBJ3q1+f5jh1vJmmbzUbN557Dfvo0A2NiOLxvH+1ee42W1auzH5iDNmJiJNrNwza1arHDakWP1gP/C0i02+nWqBF/oU1Dfx64CjiARxs3Zgdasgf4Ca0T2KZWLbbZ7Zx1Hv8RKOTjQ/s6ddiCluwBVgJeQlCyUCG6vPEGSxcuJHH5cgZMmcInP//s8utQu0wZNgKXnJ8vBSoFBuaJJJ2S6l0r90r1qLPQ4XPn2Lx/P8fNZnyAsWYzFTds4MVHH6VkQMAd5/9x/Dg+wC60WvFYtIW+L1y/TpVSpe44f8HmzVhjY9mFNqxuNFDm33956PRpAoDNgBdaT7kssPbAAQKBTc7jw4GyDgdRCQk4gFpAESASLcF7G40UFIL6UlICiAbsQlCrTBle69uXet98Qwm9nlidjlUvv0yTKlVYtWMH1fbupQhwA5j13HOsP3SImPPn2WU2owMGWyzU/PprhnXpkuZIjqZVqzKiZ09qrlhBCYOBBIOB1S+9dG/fBA93x3KrqnetZIBK1FkoKj6ewkLQDLgOBAH+Oh3RCQkYdDomfPUVJy9coG7lyrzZvz/hN24QjJakAQqj3Ty8GBlJIV9fJnz1FacvXqRB1apMfuIJbdo4WpIGKIZ2k/BMRATl0JIxQHG00seZq1dTHS/pPH726lUao80k3AF0R+tdn792jRBvb+YnJhIBlANK6fUkmM0M69qVPq1aEREdTflixfA1mQD4cOBA7DYb/0VE0KdBA/q2bMl3O3ZQQkqeRttxojnaxB2z1Xrzebcb9+ijPB0aytWYGCoWL37PU+Nzi9CNajMDJeNU6SMLVS5ZklNJSTQD5gHlgSiLhVKFChE6YQKm7dsZd/o0EZs20fPtt+nVtCl/A18CV4B3AZtOR71y5Wj76qsU2LGDcadPc37jRnq9+y69mzblIPC18/zJgE6v54XOndmHVjKIQBsv7aXXM6hNG3YDy5zHXwX0QjCyc2d2oY32eByYDfgYDIQ1acIOKdmE1tN+S6ejVlDQzdmKRQoWpGZw8M1kGxUfT6tXXqHO4cO8Fx7OsY0bGfjxx9QtV45NZjMVgBeBbUBJX1+XSTpZMX9/agYH5/kknSzNZVYVJQ0qUWehNfv3UxyYhTbW+RtADyzauhVddDQz7HY6AousVo6cOYNOp2PhqFGMNxgoB8wymfhl0iQOnDmDX1wc053nL7Za2X3yJP6+vswdMYJRej3lgHne3vw6eTJ1K1Rg9rBhjNDrqQAs8PZm7VtvEZ2URBUvL6ailTn2opUyyhYrxidDh/Kcs51vfHzY8O67BBctyuoJE3i7RAlqmUwcrlaNVRMmuLwZuuHwYWqYzUx2OOgErLBYWL57NwfPnqWV0chkoBPaaJDwxETMVus9f22zY00aT5GcsP1jUAlbSZMqfWQhIQQ2tN8zgXaDLnlKtS1FonEADinR6XT0bt6c3s2bp2pny7Fjd5xvd57/xIMP8sSDD95x7adat+ap1q1THVt74AA+QrDTGU8CUEIIBDCwbVsGtm17RztNq1blwMyZGXu9aDsfJ0ue46ETApmiFp18zr1MCf9pzx6GzZ5NRHw8rSpVYvG4cWnW+/OCjuu1/79/FBzJXz5VElFQPeos1bd5cxK8vHgSWA70AAr6+jKgTRt8ixVjgMHAcqC30UjT6tUp42Jj2qZVqiCKFGGw8/wwo5G2tWtTolChTMVTuWRJjlksPAd8B3QDfPV6CmVyircrHerV46xz6vd3wMNGI0+3bMnDjRpx0seHF53Hu5tMDGzVKtPLlh67cIEBM2bwTWwsNxwOGpw+Td+pU7Mkdk8WttI5uzH5L72S76lEfR8u37jBzn/+ubkVlq+3N/s++YT/ypfnFT8/kqpW5dCnn2IyGln/1lsUa9+epbVr06BrV5a99JLLHqbJy4sNb79NodBQltauTZOHH+ab8eMz3SM9cu4cTb298UGrU4ei3dS7EnPHlpf3pKCPD39MnYr9oYdYVqcO3Xr1Ytbzz+Pv68u2qVOxOI/36NWLmUOHZrr9bceP8zDQEvAF3rXb2Xb6NFYXa5vkNWEr1VR0RaNKH/do4YYNjP3ySyoZDJyy2Zg7bBhhzZoRXKQIO95//47z/X19ef+ZZzLcfoCfH9MGDLivGIv6+3NWSlajjSw5B7wjJf4+PvfVbkrFCxXikyFD7jheIiCAmWkcz4yiBQtyTKfDjlbrPwYUNBo9bunT7KSmoiugetT35ML164xduJCdFgt/JSSw0WLh2c8+Iyo+3t2hpdKiWjVC6talhbc3w728aGky8W6/fumOvvAU3UNCCChfntYmE8O9vOhgNDLz2Wc9fvnT7KAmy+RvGdncdgFaefOKlNL1POB85HREBNW9vKjqHMVQHyip13P++vVMLbwP2pTz7//8k1MRETQoX56O9etnWZxCCBa/+CI/7tnDuWvX+KZixVy10JFBr+fnN95gxa5dRERH83O1aoRUquTusNxGTUXPvzJS+vgS+BT4KntDyT0qlyzJiRS7gf8FRDgclC1aNJ1nppY85fzo/v20sVoZ4eVFv86dmdSvX5bFqtPp6Nm4cZa1l9MMej19WrRwdxgeJWyVmiyT36Rb+pBSbkWbZaw4BRUuzMeDB9PSaKSujw+djUYWjhyZ6dEU+//7j6379/OH2cwHDgfbzWY+/PlnrsfGZlPkSl5xx2QZhQvHLvBmuzfp79ufIUFDWPb6Mhz29FdyPH/0PG93eJv+vv0ZWHQgXzz3BUlxSXect/vH3bxY50We8H6C0TVHs2PZjux4GWnKspuJQojBwGAg0z3L3OiJVq3o1KAB565do3yxYgTew87bkXFxlNPrSb61VwwI1OuJio+nSMGCWRqvkjepqeiauBtxvBX6FsE1gxn/43jCT4Wz+MXFSIekz9t9XD4vITqBN9u+SamqpRi1bBRx1+NYMn4JNy7fYPwP42+ed3zbcaaHTafD8x145pNn2L9mPx/3/Ri/QD/qdaiX7a8vyxK1lHIuMBe0Xcizql1PVqRgwftKqA0qVOAE2tTvzsA8IfApUIByxYql80xFueWOhZ4g3yXs3z7/DUuihRdXvoivvy9129clMSaR5ZOW0318d3z90363u27WOiyJFl766SX8ArT7SwUKF+D9Hu9zas8pKoVo90S+f+t7arSqwYBPtJFYtdvU5sLRC6x4c0WOJGo16sONihQsyM+vv87UEiUoYzDwY7lyrJk0KV8NP1OyTn6ein7g1wPU61gvVUJu0acFlkQLx7Ycc/m8MwfOUDGk4s0kDVC3Q12EEOz7ZR8AVrOVI5uO0Kx3s1TPbd6nOf/s/IeE6IQsfjV3UuOo3axRxYoZnrKtKBlxx1T0fNC7vnj8IrXa1kp1rGjZoph8TVw6fglc7A5nTbJiMKZOg3qDHqETXPxb2zM04lQEdqud0tVLpzqvdI3SSIfk0j+XqPxA5ax7MWlIt0cthFgK7ASqCSEuCCEGZmtEiqJkibCV+WfsdfyN+FS94mR+gX7E3Yhz+bySlUty9uBZbNZbs11P7z2Nw+4gLlJ7XvLzb2+/QGCBm9fObhkZ9dFXSllKSuklpQyWUs7P9qgURckSN3dFT143JA8n7LQmQkkp7zpBqt2z7Yi5GsOCEQuICo/i/NHzzHt+Hjq9Dp3+tvR4WzPJKzrmxAQsVfpQlHwgbKX2f14th/gF+hEfdWfPNiE6Ic2edrLS1UszeO5gFo1exIY5GxA6QejgUIQQFCqhLYKW3HNOiEpdi06+nm9A1ixydjcqUStKPhK2En4LhagA54E8krBLVy/NxeMXUx27dv4a5ngzQdWD7vrctgPa0rJfS8L/Dce/uD/+Rf0ZUGQAbQdpywCXqFQCvZeei8cvUrN1zZvPu3T8EkInCKp69/azghr1oSj5TPsNznJI8sp8eUD9zvU5uO4gibGJN4/tWLYDo48xVXJ1xehtpGydsgSUCGDrkq1Ih6R5b22deC+TF7Xb1GbX8l2pnrNj2Q6qNquKb6Hs71GrRK0o+VTYqryTrNsPbY+XyYtpj07j0IZDbJi7geWTltNtTLdUQ/ZGVB7B7IGzb36eEJPAkpeWsO+XfRxYd4CvX/6aOYPm8Mwnz1Cg8K1JbGETwzi6+ShfjvqSo5uPsmT8Evav2U+v13vlyOtTpQ9FycfCVsG6DhDjT64ugxQILMDrG19n/vD5TH14Kn4BfnQd3ZXek3qnOs9hc6SaVq7T6ziz/wwbv9iIJdFC2dplGb18NI0fSb0+TvWW1RmzYgzLJixj/ez1FK9QnJHfjMyRyS4AIjv2ogupVEnumTIly9tVFCV7qBX53K+36L1XShmS1mOq9KEoSp4qg+RFKlErigJoyfrm9HPFo6hErSjKTR3Xq561J1KJWlGUVG6WQRSPoRK1oih3KJQPV+DzZGp4nqIod1D7M3oW1aNWFMWlsFXOBZ0Ut1KJWlGUuyoUjSqDuJkqfSiKcleqDOJ+qketKEqGqDKI+6hErShKhqkyiHuo0oeiKBmmyiDuoXrUiqJkmiqD5KwMJWohRCchxAkhxEkhxMvZHZSiKJ5PlUFyTkZ2IdcDnwGdgZpAXyFE+lsmKIqSp6XaOFfJVhnpUTcGTkopT0spLcC3QI/sDUtRlNziZs9ayTYZuZlYGjif4vMLQJPbTxJCDAYGOz+NE717n7j/8LJVUeCau4PIQer15m3q9eZ+5Vw9kJFEndZ93Tv+fkop5wJzMxGUWwkh9rjaTSEvUq83b1OvN2/LSOnjAlAmxefBwKXsCUdRFEW5XUYS9W6gihCighDCCPQBVmdvWIqiKEqydEsfUkqbEGI4sA7QAwuklEezPbLsl2vKNFlEvd68Tb3ePCxbdiFXFEVRso6amagoiuLhVKJWFEXxcPk2UQsh9EKI/UKIn90dS3YTQpwRQhwWQhwQQuxxdzzZTQgRIIRYIYQ4LoT4WwjRzN0xZRchRDXn9zX5X4wQYpS748pOQojRQoijQogjQoilQghvd8eU3fJtjVoIMQYIAfyllN3cHU92EkKcAUKklHltgkCahBCLgD+klPOcI5V8pZRRbg4r2zmXe7gINJFSnnV3PNlBCFEa2AbUlFImCiG+A9ZIKb90b2TZK1/2qIUQwUBXYJ67Y1GylhDCH2gFzAeQUlryQ5J2agecyqtJOgUD4COEMAC+5IN5HfkyUQMzgPFAfllORgLrhRB7nVP987KKwFVgobO0NU8I4efuoHJIH2Cpu4PITlLKi8A04BxwGYiWUq53b1TZL98laiFEN+CKlHKvu2PJQS2klA3RVkAcJoRo5e6AspEBaAjMllI2AOKBPL80r7PE0x1Y7u5YspMQIhBtUbgKQBDgJ4To796osl++S9RAC6C7s277LdBWCLHEvSFlLynlJef/V4BVaCsi5lUXgAtSyj+dn69AS9x5XWdgn5Qywt2BZLNQ4D8p5VUppRVYCTR3c0zZLt8lainlK1LKYCllebS3ir9LKfPsX2QhhJ8QomDyx0AH4Ih7o8o+Uspw4LwQoprzUDvgmBtDyil9yeNlD6dzQFMhhK8QQqB9f/92c0zZTu2ZmPeVAFZpP9MYgG+klGvdG1K2GwF87SwHnAaecXM82UoI4Qu0B4a4O5bsJqX8UwixAtgH2ID95IPp5Pl2eJ6iKEpuke9KH4qiKLmNStSKoigeTiVqRVEUD6cStaIoiodTiVpRFMXDqUStKIri4VSiVhRF8XD/BwmsYCWY+Y3mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ray\n",
    "import codeflare.pipelines.Datamodel as dm\n",
    "import codeflare.pipelines.Runtime as rt\n",
    "from codeflare.pipelines.Datamodel import Xy\n",
    "from codeflare.pipelines.Datamodel import XYRef\n",
    "from codeflare.pipelines.Runtime import ExecutionType\n",
    "\n",
    "ray.shutdown()\n",
    "ray.init()\n",
    "\n",
    "n_neighbors = 1\n",
    "\n",
    "dataset = datasets.load_iris()\n",
    "X, y = dataset.data, dataset.target\n",
    "\n",
    "# we only take two features. We could avoid this ugly\n",
    "# slicing by using a two-dim dataset\n",
    "X = X[:, [0, 2]]\n",
    "\n",
    "X_train, X_test, y_train, y_test = \\\n",
    "    train_test_split(X, y, stratify=y, test_size=0.7, random_state=42)\n",
    "\n",
    "h = .01  # step size in the mesh\n",
    "\n",
    "# Create color maps\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "names = ['KNN', 'NCA, KNN']\n",
    "\n",
    "pipeline = dm.Pipeline()\n",
    "node_scalar = dm.EstimatorNode('scaler', StandardScaler())\n",
    "node_knn = dm.EstimatorNode('knn', KNeighborsClassifier(n_neighbors=n_neighbors))\n",
    "node_nca = dm.EstimatorNode('nca', NeighborhoodComponentsAnalysis())\n",
    "node_knn_post_nca = dm.EstimatorNode('knn_post_nca', KNeighborsClassifier(n_neighbors=n_neighbors))\n",
    "pipeline.add_edge(node_scalar, node_knn)\n",
    "pipeline.add_edge(node_scalar, node_nca)\n",
    "pipeline.add_edge(node_nca, node_knn_post_nca)\n",
    "\n",
    "# create training input\n",
    "train_input = dm.PipelineInput()\n",
    "train_input.add_xy_arg(node_scalar, dm.Xy(X_train, y_train))\n",
    "\n",
    "pipeline_fitted = rt.execute_pipeline(pipeline, ExecutionType.FIT, train_input)\n",
    "\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                     np.arange(y_min, y_max, h))\n",
    "\n",
    "name = names[0]\n",
    "# create test input\n",
    "test_input = dm.PipelineInput()\n",
    "test_input.add_xy_arg(node_scalar, dm.Xy(X_test, y_test))\n",
    "\n",
    "knn_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_knn)[0])\n",
    "knn_score = ray.get(rt.execute_pipeline(knn_pipeline, ExecutionType.SCORE, test_input)\n",
    "                    .get_xyrefs(node_knn)[0].get_Xref())\n",
    "\n",
    "# Plot the decision boundary. For that, we will assign a color to each\n",
    "# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "# create predict input\n",
    "meshinput = np.c_[xx.ravel(), yy.ravel()]\n",
    "meshlabel = np.ones(meshinput.shape[0]).shape\n",
    "predict_input = dm.PipelineInput()\n",
    "predict_input.add_xy_arg(node_scalar, dm.Xy(meshinput, meshlabel))\n",
    "\n",
    "Z = ray.get(rt.execute_pipeline(knn_pipeline, ExecutionType.PREDICT, predict_input)\n",
    "                         .get_xyrefs(node_knn)[0].get_Xref())\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.rcParams['pcolor.shading'] ='nearest'\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8)\n",
    "\n",
    "# Plot also the training and testing points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"{} (k = {})\".format(name, n_neighbors))\n",
    "plt.text(0.9, 0.1, '{:.2f}'.format(knn_score), size=15,\n",
    "         ha='center', va='center', transform=plt.gca().transAxes)\n",
    "\n",
    "name = names[1]\n",
    "nca_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_knn_post_nca)[0])\n",
    "nca_score = ray.get(rt.execute_pipeline(nca_pipeline, ExecutionType.SCORE, test_input)\n",
    "                    .get_xyrefs(node_knn_post_nca)[0].get_Xref())\n",
    "\n",
    "Z = ray.get(rt.execute_pipeline(nca_pipeline, ExecutionType.PREDICT, predict_input)\n",
    "                         .get_xyrefs(node_knn_post_nca)[0].get_Xref())\n",
    "\n",
    "# Put the result into a color plot\n",
    "Z = Z.reshape(xx.shape)\n",
    "plt.figure()\n",
    "plt.pcolormesh(xx, yy, Z, cmap=cmap_light, alpha=.8)\n",
    "\n",
    "# Plot also the training and testing points\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor='k', s=20)\n",
    "plt.xlim(xx.min(), xx.max())\n",
    "plt.ylim(yy.min(), yy.max())\n",
    "plt.title(\"{} (k = {})\".format(name, n_neighbors))\n",
    "plt.text(0.9, 0.1, '{:.2f}'.format(nca_score), size=15,\n",
    "         ha='center', va='center', transform=plt.gca().transAxes)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "ray.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
 "nbformat": 4,
 "nbformat_minor": 1
}
